dot-skills

Chrome Extensions UX/UI design and implementation guidelines for popups, side panels, content scripts, and options pages. Triggers on tasks involving browser extension UI, manifest v3, chrome APIs.

Feature specification and planning guidelines for software engineers. This skill should be used when writing PRDs, defining requirements, managing scope, prioritizing features, or handling change requests. Triggers on tasks involving feature planning, specification writing, stakeholder alignment, or scope management.

Xcode setup and tooling guidance for iOS 26 / Swift 6.2 clinic modular MVVM-C projects covering project configuration, SwiftData container wiring, testing, debugging, profiling, and distribution workflows. Use when configuring App-target infrastructure or day-to-day tooling around clinic architecture modules.

Clean Architecture principles and best practices from Robert C. Martin's book. This skill should be used when designing software systems, reviewing code structure, or refactoring applications to achieve better separation of concerns. Triggers on tasks involving layers, boundaries, dependency direction, entities, use cases, or system architecture.

Use when writing, reviewing, or refactoring code for maintainability and readability. Triggers on code reviews, naming discussions, function design, error handling, and test writing. Based on Robert C. Martin's Clean Code handbook with modern corrections.

JavaScript style and best practices based on Google's official JavaScript Style Guide. This skill should be used when writing, reviewing, or refactoring JavaScript code to ensure consistent style and prevent common bugs. Triggers on tasks involving JavaScript, ES6, modules, JSDoc, naming conventions, or code formatting.

jscodeshift codemod development best practices from Facebook/Meta. This skill should be used when writing, reviewing, or debugging jscodeshift codemods. Triggers on tasks involving AST transformation, code migration, automated refactoring, or codemod development.

Codemod (JSSG, ast-grep, workflows) best practices for writing efficient, safe, and maintainable code transformations. This skill should be used when writing, reviewing, or debugging codemods, AST transformations, or automated refactoring tools. Triggers on tasks involving codemod, ast-grep, JSSG, code transformation, or automated migration.

Knip dead code detection best practices for JavaScript and TypeScript projects. Use when configuring Knip, analyzing unused code, setting up CI integration, or cleaning up codebases. Triggers on knip.json, dead code, unused exports, unused dependencies, bundle optimization.

MSW (Mock Service Worker) best practices for API mocking in tests (formerly test-msw). This skill should be used when setting up MSW, writing request handlers, or mocking HTTP APIs. This skill does NOT cover general testing patterns (use test-vitest or test-tdd skills) or test methodology.

MUI Base UI style guidelines for building headless React component libraries (formerly headless-ui-style). This skill should be used when creating unstyled UI components, compound components with render props, accessibility-first patterns, or component libraries that separate logic from styling. Extracted from the MUI Base UI codebase (github.com/mui/base-ui).

Playwright testing best practices for Next.js applications (formerly test-playwright). This skill should be used when writing, reviewing, or debugging E2E tests with Playwright. Triggers on tasks involving test selectors, flaky tests, authentication state, API mocking, hydration testing, parallel execution, CI configuration, or debugging test failures.

The Twelve-Factor App methodology for building scalable, maintainable cloud-native applications. Use when designing backend services, APIs, microservices, or any software-as-a-service application. Triggers on deployment patterns, configuration management, process architecture, logging, and infrastructure decisions.

37signals Rails coding principles and conventions from DHH, Jorge Manrubia, and the Fizzy/Basecamp/HEY codebase. This skill should be used when writing, reviewing, or refactoring Ruby on Rails code following the 37signals philosophy — vanilla Rails, CRUD controllers, rich domain models, concerns, no service objects, Hotwire, Turbo, Stimulus, Solid Queue, Solid Cache, Solid Cable, multi-tenancy, Minitest, custom auth, or DHH conventions.

Use when implementing, reviewing, or debugging a Gherkin acceptance-test pipeline with mutation testing. Covers parser, JSON IR, generator, runtime, step handlers, test runner, mutator, value mutation rules, execution, result classification, reporting, project layout, conformance, and agent setup. Based on Uncle Bob's Acceptance Pipeline Specification. Trigger even when the user mentions Gherkin parsing, acceptance test generation, mutation testing for acceptance tests, or building a portable test pipeline.

Designs new features, extensions, or modifications to Uncle Bob's Acceptance Pipeline Specification — new mutation strategies, Gherkin syntax support, report formats, pipeline stages, IR fields, or handler patterns. Trigger when someone asks "how would I add X to the acceptance pipeline" or discusses spec-level changes to the parser, generator, runtime, mutator, or reporter components — even if they don't explicitly say "feature design." Works in tandem with the acceptance-pipeline-catalog skill, which provides the baseline spec reference.

Use this skill to gate Elixir/OTP systems on BEAM runtime architecture with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 23 decidable rules covering supervision and failure design (restart strategy vs child dependencies, blocking init, durability in terminate, restart amnesia, unsupervised fire-and-forget), backpressure (cast on externally-driven ingest, push pipelines without demand, silenced or retried call timeouts, unbounded fan-out), event delivery semantics (PubSub treated as durable, jobs enqueued outside the creating transaction, non-idempotent consumers under at-least-once delivery, cross-source ordering, telemetry handlers that block or detach), shared-state races (check-then-act on ETS/Registry, hot reads through one mailbox, persistent_term churn), distribution (global singletons in netsplits, node-local uniqueness assumed cluster-wide), and runtime mechanics (wall-clock durations, sub-binary leaks, minted atoms). Verdicts only, never fixes.

Use this skill to gate DDD and ubiquitous-language quality with a pass/fail adversarial review — a single blind reviewer subagent judges a diff, a module/bounded context, or a DSL/semantic-model surface against 20 decidable rules across glossary lifecycle (a recorded ubiquitous language is required once domain concepts exist — absence is FAIL; code must conform; entries stay live and carry business meaning), language consistency (one name per concept, one meaning per name, no Manager/Helper/Data names on domain rules, transitions named as operations, one vocabulary across code, tests, docs, UI copy), domain model integrity (anemic models, valid-on-construction, value types over primitives, named states over flag piles), context boundaries (foreign-model reach, vendor-type absorption, infrastructure in the domain, multiple writers), and DSL surfaces. Language-agnostic; repeated runs converge code, team, and stakeholders on one recorded vocabulary. Verdicts only.

Use this skill to gate Elixir, OTP, Ecto, and Phoenix code with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 19 decidable rules that catch architecture ported from an alien paradigm, covering enterprise layering (repository wrappers over Ecto, service-object tiers, DI behaviours with a single implementation, logic trapped in effectful callbacks), processes as objects (GenServer-per-entity, Agents as mutable variables, leaked message protocols, singleton managers), anemic data (bare maps as entities, boolean-flag state, hand-rolled type dispatch, 32+-field structs), defensive control flow (raise/rescue as branching, nil-guard swallowing, raw input past the boundary), transliterated loops, and needless metaprogramming (macro DSLs for data, use-as-import, compile-time coupling). Trigger it before merging Elixir work, or to check agent-authored code. It renders verdicts only, never fixes; Ecto-dependent rules go N/A without Ecto.

Use this skill to gate iOS SwiftUI design and UX with a pass/fail adversarial review — a single blind reviewer subagent judges screens, diffs, or features against 56 decidable HIG-derived rules, working backwards from mandatory rendered evidence, simulator screenshots (light, dark, accessibility text size) and interaction recordings tiled into filmstrips. Covers navigation/IA (tab bars, five tabs max, system Back), modality and flow grammar (sheets, alerts, unsaved-content protection), layout (44pt targets, safe areas, Dynamic Type), color and contrast, typography floors, Liquid Glass, feedback states (empty, loading, error, launch), motion — filmstrip-primary except the spring-curve and Reduce Motion checks, so code alone never prescribes an unseen animation, and gratuitous decoration fails — plus haptics and craft. Fixes must be the minimal change, removal preferred over addition. Trigger before merging user-facing SwiftUI work. Verdicts only, never fixes; targets without a SwiftUI UI surface abort.

Use this skill to gate Phoenix LiveView realtime UIs with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 32 decidable rules covering state ownership and lifecycle (patch vs remount, callback load placement, connected-mount guards, form recovery), realtime data flow (broadcast placement, scoped topics, presence mechanisms), async responsiveness (blocking external calls, socket-copying closures, lifecycle-owned tasks, rendered failure states), render and wire efficiency (the constructs that silently disable HEEx change tracking), streams (growing collections in assigns, the stream DOM contract, bounded infinite scroll), component and context boundaries, client trust (per-event authorization, scoped lookups, live_session boundaries, revocation disconnects), and mechanism-presence interaction feedback (JS commands, in-flight feedback, debounce, hook contracts, overlay focus). Verdicts only, never fixes.

Use this skill to gate Python code (floors 3.10+, rules verified through 3.14) with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 20 decidable rules hunting two failure modes. First, code modern Python makes unnecessary — branch ladders over match/registries, hand-written init/repr/eq over dataclasses, TypeVar ritual over PEP 695, typing.Optional over PEP 604 unions, os.path over pathlib, hand-rolled stdlib batteries, deprecated utcnow, orphan create_task over TaskGroup. Second, legacy-pattern propagation — single-implementation ABCs, pass-through layers, single-method classes, concrete-inheritance reuse, boolean-forked functions, shapeless payloads and parameter clumps — judged as if greenfield; consistency with legacy code is not PASS evidence. A version probe reads the target's Python floor, marks rules above it N/A, and fetches the official what's-new delta when the floor exceeds the verified version. Verdicts only, never fixes.

Use this skill to gate Elixir code built on the Reactor orchestration library (~> 1.0) with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 28 decidable rules covering saga compensation and undo (side effects without undo, cleanup in the wrong callback, compensate returns that roll back vs continue, non-idempotent undo), retry discipline (uncapped retries under the max_retries infinity default, retrying business failures, missing backoff), dependency and data flow (lexical-order assumptions, context smuggling, missing return), step contracts (invalid run/3 returns, halt misused as failure, guard/where confusion, side effects in inline fns), composition (Reactor.run inside steps instead of compose, Enum loops over map steps, case over switch, unbounded recurse), concurrency (serial-by-default map, sandbox tests left async, process-context loss), and middleware contracts. Verdicts only, never fixes.

Use this skill when reviewing or refactoring existing Rust code that carries an alien mental model — OO/enterprise ceremony from Java/C#, garbage-collected object graphs, exception-style control flow, or imperative loops ported onto the borrow checker. It is the adversarial, architecture-level counterpart to greenfield idiom advice — it names the paradigm the code betrays and prescribes the deep refactor that collapses it, up to deleting whole layers (single-impl DI traits, Deref inheritance, Manager/Service structs, reflexive builders, Rc<RefCell> webs, clone-until-it-compiles, bool/String state machines, sentinel returns, catch_unwind try/catch, reflexive Box<dyn>, blocking calls inside async, fire-and-forget spawns). Every rule is grounded in the codex-rs production workspace (openai/codex) — the prescriptions are what that codebase actually does and lint-enforces. Applies whenever the work is "make this Rust actually Rust", "flatten this architecture", or a pedantic review of code fighting the language.

Use this skill to gate Swift language code with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 36 decidable rules covering concurrency (unresumed continuations, cancellation checks, async let, TaskGroup, @concurrent), property invariants (stored-derived state, private(set), observers skipped in init, discard self), error handling (underlying errors preserved, rethrows, Result.get(),

Use this skill to gate SwiftUI code with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 31 decidable rules — data modeling and observation (@Observable over ObservableObject, @State ownership, task(id:) re-init guards, environment closures and high-frequency values), view update cost (computed-var extraction, whole-model dependencies, init side effects, uncached derivations, structs on rows), structural identity (applyIf branching, AnyLayout), task lifecycle (.task over onAppear+Task, scalar ids, @concurrent offloading), lists and geometry (ForEach view count, AnyView rows, GeometryReader measurement, feedback loops, visualEffect), animation scope, and accessibility (button semantics, style protocols, accessibilityRepresentation, semantic styling, ScaledMetric spacing). Trigger before merging SwiftUI work or to audit agent-authored views. Verdicts only, never fixes; targets without a SwiftUI surface abort.

Use this skill to gate TanStack Start plus TypeScript web-app changes with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 22 decidable rules covering client/server boundary leaks, server-function and server-route usage, auth and security, SSR data loading, boundary type safety, and compiler config. Trigger it before merging TanStack Start work, when asked to gate, adversarially review, or pass/fail a Start or TanStack codebase, or as a final check on agent-authored Start features. It renders verdicts only and never fixes the work; for teaching-style review feedback use a distillation skill instead.

Use this skill to gate TypeScript and React application code with a pass/fail adversarial review of design-pattern usage — a single blind reviewer subagent judges a diff or file set against 18 decidable rules covering implicit state machines (boolean-flag lifecycles, useEffect chains, stored derived state, non-exhaustive union matches) and over-engineered OO/enterprise ports (getInstance singletons, factory and builder classes, single-method strategy classes, State/Visitor hierarchies, event buses inside a React tree, pass-through repositories, DI containers, single-implementation interfaces, component inheritance, logic-only HOCs, static-only classes, trivial accessors). Trigger it before merging TS/React work, when asked to gate or pass/fail pattern usage, or as a check that agent-authored code is not porting Java/C# idioms. It renders verdicts only; for teaching-style guidance use implementation-design-patterns or implementation-functional-patterns.

Use this skill to gate Zod 4 schema code in TypeScript and TanStack Start apps with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 24 decidable rules covering silent Zod 3-to-4 semantic breaks (defaults, enum-keyed records, boolean coercion), removed APIs (including the z.interface hallucination), unified error customization, deprecated method forms, recursion and codec composition, adapter-free TanStack Start validators, and packaging. Trigger it before merging Zod schema work, when asked to gate, adversarially review, or pass/fail Zod usage, or as a currency check that agent-authored schemas use the latest Zod 4.x surface. It renders verdicts only and never fixes the work; for teaching-style Zod feedback use the curated zod skill instead.

Algorithmic complexity (Big-O) review — finding nested loops, N+1 queries, exponential recursion, quadratic string builds, and other accidental complexity blowups. Covers Python, JavaScript/TypeScript, Java, Go, and similar languages. Use whenever writing, reviewing, or refactoring code where Big-O matters. Trigger even when the user doesn't mention "Big-O" explicitly — if they're reviewing code for performance, refactoring a hot path, asking "why is this slow," or working with data that scales (loops, recursions, collections, ORM access), apply this skill to classify the time/space complexity and suggest the fix. Especially trigger on tasks like "review for performance," "find slow code," "make this faster," "this code is O(n²)," or when reading code that processes collections.

Entry point for MCP server development — interrogates the user about their use case, determines the right deployment model (remote HTTP, MCPB, local stdio), picks a tool-design pattern, and hands off to specialized skills. Triggers when the user asks to "build an MCP server", "create an MCP", "make an MCP integration", "wrap an API for Claude", "expose tools to Claude", "make an MCP app", or discusses building something with the Model Context Protocol.

Interactive UI rendered inside ChatGPT or Claude — OpenAI Apps SDK apps, MCP Apps (the @modelcontextprotocol/ext-apps standard), or MCP-UI components, with a Next.js/React server. Covers the MCP tool and resource architecture, the window.openai / ui-bridge data flow, widget state, sandbox/CSP security, display modes, visual design, and directory submission. Trigger when building, reviewing, or designing such apps — even when the user only says "chat app", "ChatGPT widget", "Claude app", "render UI in chat", "window.openai", "createUIResource", "outputTemplate", or "MCP app UI" without naming a specific SDK.

Chrome Extensions (Manifest V3) performance and code quality guidelines. Use when writing, reviewing, or refactoring Chrome extension code including service workers, content scripts, message passing, storage APIs, TypeScript patterns, and testing.

Produces a design-plan (living document like an exec-plan) that maps an app domain to feature groups using Apple Design DNA patterns. Each feature group becomes a milestone buildable in one ios-taste session. Use when the user describes an app idea, domain, or workflow and needs a structured plan before building. Triggers on "plan this app", "what features does X need", "design plan", "feature breakdown", "what screens do I need", or any pre-build planning question. Also trigger when the user provides workflow notes or user interview results. CRITICAL: This skill produces a DESIGN-PLAN document only. It does NOT generate SwiftUI code, layouts, or visual design.

ast-grep rule writing and usage best practices. This skill should be used when writing, reviewing, or debugging ast-grep rules for code search, linting, and transformation. Triggers on tasks involving YAML rules, pattern syntax, meta variables, constraints, or code rewriting.

Use this skill when writing, debugging, or reviewing ast-grep patterns, YAML rules, or codemods against TypeScript or React (.ts/.tsx) code — searching for JSX elements, props, hooks, imports, or type constructs, and rewriting them. Covers the TS/React-specific traps — the tsx-vs-typescript language split, JSX and TypeScript node kinds, fragment matching, rewrites, and the @ast-grep/napi API. Complements the general-purpose ast-grep skill (rule mechanics) — reach for this one whenever the target code is TypeScript or React.

Audio forensics and voice recovery guidelines for CSI-level audio analysis. This skill should be used when recovering voice from low-quality or low-volume audio, enhancing degraded recordings, performing forensic audio analysis, or transcribing difficult audio. Triggers on tasks involving audio enhancement, noise reduction, voice isolation, forensic authentication, or audio transcription.

Migrates React UI code to Base UI (`@base-ui/react`) — replacing bespoke modals, custom dropdowns, raw `<dialog>`/`<select>` elements, ad-hoc popovers/menus/tooltips, or other component libraries (Radix UI, Headless UI, Reach UI). Ships a 37-component catalog (snapshotted from base-ui.com/llms.txt) and scripts to refresh it, scan for migration candidates, and verify the migration compiles. Triggers on phrases like "migrate to base-ui", "use base-ui instead of X", "replace this dialog/popover/menu with base-ui", or when scanning a React codebase for components Base UI can replace. Trigger even if the user only mentions one component (e.g., "swap this modal for base-ui dialog") — the workflow scales from one file to a whole repo.

Better Auth in TypeScript — setting up the auth instance, picking adapters, wiring framework route handlers, configuring sessions and cookies, adding plugins (2FA, organization, admin, magicLink, JWT), or porting from NextAuth/Auth.js, Clerk, Auth0, or Supabase Auth. Covers Next.js, SvelteKit, Hono, Express, Nuxt, Astro, and React/Vue/Svelte clients. Trigger when writing, reviewing, or migrating Better Auth code — and even when the user doesn't explicitly mention Better Auth but is working on TypeScript authentication, session cookies, OAuth providers, or auth-library migration. Contains 42 rules organized by impact across 8 categories.

Scaffolds a Better Auth setup in a Next.js (App Router) + Drizzle project — lib/auth.ts, lib/auth-client.ts, the /api/auth/[...all] route handler, middleware.ts, .env.example, and a permissions module. Produces convention-enforced templates for three plugin presets (minimal, social, advanced with twoFactor+magicLink). Trigger even when the user doesn't explicitly say "scaffold" — phrases like "set up Better Auth", "wire up auth", "initialize auth in this project", or "add auth to Next.js" should pull this in. Pairs with the `better-auth` skill, which covers the rules these templates encode.

Multi-pass PR bug review — 5 parallel passes, majority voting, independent Opus validation, and resolution rate tracking. Trigger on PR review, bug finding, code review, "review this PR", "check for bugs", "find issues in this PR", or /bug-review. Also trigger on /bug-review:resolve to classify whether findings were fixed at merge time, and /bug-review:report for resolution rate stats. Even if the user just says "review this" while on a PR branch, trigger this skill.

Use when writing, reviewing, or refactoring TypeScript or React code for craftsmanship — naming, function and component shape, error handling, data modeling, tests, and abstraction. Translates Robert C. Martin's Clean Code principles into modern TS+React idioms (TS 5.x, React 19), with first-class "When NOT to apply" guidance and a Meta category for principle conflicts (DRY vs SRP, small functions vs deep modules, type safety vs ergonomics). Triggers on code review, refactoring for clarity, naming, function/component design, "is this clean?", "make this more readable", "right abstraction?" — even when the user doesn't say "clean code". Does NOT cover React-specific APIs (RSC, hooks API surface) — use the `react` skill. Does NOT cover TS compiler perf or tsconfig — use the `typescript` skill.

Designing CLIs that AI agents will invoke — non-interactive flags, layered --help with examples, stdin/pipeline composition, actionable errors, idempotency, dry-run, destructive-action safety, and predictable command structure. Use when designing, building, or reviewing a command-line tool that AI agents or automation will invoke. Trigger even if the user doesn't explicitly say "agent-friendly" — apply whenever they are writing `--help` text, adding a new subcommand, designing error messages, or reviewing a CLI's UX.

Black-box CLI grading harness — runs a test suite against a target CLI and reports per-rule pass/fail from the cli-for-agents 45-rule catalog. Use when reviewing, auditing, or grading a command-line tool for agent-friendliness. Trigger even if the user doesn't explicitly say "agent-friendly" — apply whenever they ask "is mycli good for agents?", "review this CLI", "grade my cli against the rules", "check if this tool is safe to automate", or "audit command-line design". Companion to the cli-for-agents distillation skill.

Guided, scripted pipeline for running JSX/TSX/React codemods safely across large legacy codebases. Use when you need to scaffold a codemod, dry-run it, validate its findings, and apply it across many files (50 to 100k+) without breaking the build. Walks the full inner/outer loop with the Codemod CLI (JSSG, ast-grep, workflows). Triggers on large-scale refactor, legacy React migration, codemod a prop/API change, migrate components across the codebase, run a codemod safely, batched/resumable codemod apply, dry-run a codemod.

Patterns and anti-patterns for using OpenAI Codex Goals — the persistent objectives feature introduced in Codex 0.128.0. Use this skill whenever writing, reviewing, or debugging a `/goal` invocation, deciding whether a task should be a Goal at all, drafting a research Goal that needs an evidence ledger, or diagnosing a Goal that completed against the wrong surface. Triggers on `/goal`, "Codex Goal", "Codex goals", "persistent objective", "evidence-based completion", "iteration policy", "blocked stop condition", or any user message describing a multi-turn Codex task with a defined finish line. Trigger even if the user doesn't explicitly mention Goals — if they're typing "/goal" or asking Codex to "keep going until X", this skill applies.

Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when the user asks to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.

Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH). Trigger on tasks involving "what's the right algorithm for…", performance-critical code, code with nested loops over the same input, recursive solutions, shortest-path / scheduling / matching / DP problems, code review for accidental O(n²) blowup, and any "how do I do X at scale / on a stream / without enough RAM" question — even if the user doesn't explicitly mention "algorithm" or "complexity."

On-demand pattern extraction from a specific GitHub codebase, given a focused query — "how does shadcn/ui implement the design system", "how does opencode use effect-ts", "how does base-ui handle composition" — when no pre-distilled static rule pack exists yet. Distills the generic pattern-extraction moves — classify the query before grepping (component / composition / state / effect / error / build / routing), grep before reading whole files, treat tests and examples/ as canonical intent, follow imports outward for the public surface, follow usages inward for variants, filter boilerplate / legacy / test scaffolding to surface load-bearing code, and capture findings to /knowledge/libraries/ for reuse. Dynamic light sibling of static code-atlas skills (opencode-ts, openai-codex-rust-patterns, nextjs-ppr-patterns). Triggers on "show me how <library> implements X", "find the <pattern> in <repo>", "distill <library>", and ad-hoc /distill-<library>-style invocations.

Rendering and perception layer for codebase-as-geohash-map visualisations — choosing what to encode on colour/size/position, picking perceptually honest colour scales (viridis/OKLCH, not rainbow), drawing tens of thousands of cells on Canvas2D + WebGL/deck.gl inside a 16ms frame budget, placing and decluttering labels, GPU or spatial-index picking, camera animation and level-of-detail crossfades, and keyboard plus screen-reader accessibility for the canvas. Sits on top of geohash-spatial-code-maps, which owns the geohash encoding, projection, tiling, and navigation math. Also covers nature-inspired rendering — Voronoi, circle packing, phyllotaxis, metaball hulls, edge bundling. Trigger even when the user does not say "visualisation" — if the work involves drawing, colouring, labelling, animating, or making navigable a code map, spatial heatmap, or large cell/point layer on the web, this is the skill.

Code simplification skill for improving clarity, consistency, and maintainability while preserving exact behavior. Use when simplifying code, reducing complexity, cleaning up recent changes, applying refactoring patterns, or improving readability. Triggers on tasks involving code cleanup, simplification, refactoring, or readability improvements.

Mapping an unfamiliar codebase into feature/business domains — answering "what is this about", "which files implement feature X", "where is the architectural spine", or reviewing a refactor that crosses module boundaries. 47 algorithms across 9 categories — graph construction (omnipresent filter, multilayer, SCC), lexical preprocessing (Samurai, TF-IDF), community detection (Leiden, Infomap, SBM, MCL, Walktrap, spectral, HDBSCAN), architecture recovery (Bunch+MQ, ACDC, Limbo, Reflexion, DSM), topic modelling (LDA, LSI, NMF, HDP), evolutionary coupling (Gall, ROSE), information-theoretic (NCD, MI, MDL, naturalness), centrality (PageRank, HITS, betweenness, TextRank), validation (MoJoFM, ARI/NMI, resolution limit, consensus, co-change prediction, ablation). Trigger without explicit "clustering" mention — codebase grokking, dependency mapping, domain extraction, architecture-recovery validation all apply.

Corrects the wrong defaults a model has when building Datadog dashboards, verified against Datadog's docs in July 2026. Use when creating, editing, or reviewing a Datadog dashboard — choosing widgets, writing metric/log/span queries, or emitting widget JSON. Covers the queries that render a plausible number and are still wrong — `.as_count()` is appended automatically in the graph editor but never through the API, so programmatic counts silently average; `p95` resolves only on distribution metrics, and averaging one is an average of averages; ratios need `.as_count()` on both sides or they divide interpolated averages. Also covers grounding queries in metrics that exist rather than invented names, wire type strings that diverge from UI names (Pie Chart is `sunburst`, Table is `query_table`), and Datadog's own layout standard. Assumes the Datadog MCP server is connected. NOT for Terraform or raw Dashboard API management, monitor and SLO authoring, or non-Datadog observability tools.

Comprehensive debugging methodology for finding and fixing bugs (formerly debugging). This skill should be used when debugging code, investigating errors, troubleshooting issues, performing root cause analysis, or responding to incidents. Covers systematic reproduction, hypothesis-driven investigation, and root cause analysis techniques. Use when encountering exceptions, stack traces, crashes, segfaults, undefined behavior, or when bug reports need investigation.

Structured UI design review — existing code (React/JSX, CSS, Tailwind) and, when behaviour matters, the running app in a real browser — reported as a prioritised Before / After / Why table. Covers visual hierarchy, spacing, typography, colour & contrast, component states, motion, responsiveness, accessibility, multi-page flow & navigation, and interaction continuity — grounded in Refactoring UI and Emil Kowalski's principles. For animation/jank/FPS, focus order, and cross-page UX it can drive Chrome via chrome-devtools-mcp to capture what a screenshot can't. Trigger when the user asks to "review this UI", "design review", "critique this component/screen/page or multi-page flow", asks why something "looks off", "looks AI-generated", or "looks like a wireframe", or wants to raise visual polish. For building UI from scratch use web-taste; for the full animation set see emilkowal-animations.

Reverse-engineering a Sketch file (or Figma export with similar shape) into pixel-perfect React + CSS — the iteration mental model, tree reconstruction, layout inference algorithms, geometry math, visual-regression diffing, and the style/typography/path conversions that make "improvement without regression" enforceable. Trigger even if the user doesn't explicitly mention "algorithms" but is converting a design source into web code, building a design-to-code pipeline, or struggling to make incremental fidelity improvements without breaking previously-converted output.

Inventing deterministic metrics — turning a fuzzy property like 'maintainability', 'risk', or 'how reducible this code is' into a deterministic, computable number an agent can trust and optimize. Covers the path from construct to adoption — operationalizing the construct, confronting computability limits (Kolmogorov, Rice) with sound proxies, picking the right measurement scale, proving properties (monotonicity, invariance, the Weyuker/Briand axioms), guaranteeing determinism, establishing construct validity (not just LOC in disguise), and hardening against Goodhart-style gaming when an agent optimizes the metric. Trigger when designing, reviewing, or validating a quantitative metric, score, measure, or index — and even when the user doesn't say 'metric' but wants to quantify, score, rank, or measure code/behavior, build a deterministic optimization target, or invent a measure for something previously unquantified (e.g., behavior-preserving codebase-size reduction).

Create well-structured RFCs and technical proposals for software projects. Use this skill whenever the user wants to write an RFC, technical proposal, design doc, architecture doc, or system design overview. Also trigger when the user says things like "write an RFC", "I need to propose a new system", "create a technical proposal", "document the architecture", "write up the design", "I need a design doc", or "explain the system architecture in a doc". Even if they just say "RFC", "design doc", or "arch doc", use this skill. Covers both RFCs (proposing what to build) and architecture docs (documenting an existing codebase).

PlantUML diagram quality on the agent-uml collaborative canvas — three tiers: rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) — even when the task seems simple, since a missing `as alias` makes elements un-annotatable and a skinparam mismatch makes diagrams unreadable.

Use whenever writing, editing, restructuring, or reviewing technical documentation — READMEs, API docs, guides, tutorials, reference, onboarding, or developer docs of any kind — to apply the Diátaxis framework, which splits content into four modes (tutorials, how-to guides, reference, explanation) that each serve a distinct user need. Trigger even when the user just says "write docs", "document this", "improve the README", or "our docs are confusing" without naming Diátaxis or documentation types. Use it to decide WHAT KIND of doc to write (via the compass), to diagnose docs that feel bloated, mix instruction with reference, or leave users unable to get started or find facts, and to improve docs in small safe iterations. Especially when you are unsure whether something belongs in a tutorial vs how-to vs reference vs explanation, or when one page is trying to do all four at once.

Django backend patterns for recommendation services (AWS Personalize, Databricks Model Serving, internal microservices) and OpenSearch-backed search/feed endpoints. Covers fan-out orchestration (asyncio.gather, deadline propagation, partial results, async client reuse), external service protection (timeouts, circuit breakers, jittered retry, bulkheads, rate limits), OpenSearch query patterns (search_after, _source filtering, function_score, aliases, routing, bool.filter), result blending (score normalization, MMR, dedup, cold-start), Redis caching (stampede protection, model-versioned keys, two-tier, negative), resilience (partial-response envelope, stale-on-error, graceful degradation), async (sync_to_async, async ORM, uvicorn, contextvars, disconnect cancellation), and DRF response shape (cursor pagination, ETag, throttling). Use when building, reviewing, or refactoring such a Django backend. Triggers even without explicit "scale" cues. Includes 5 scaffolding templates.

Dockerfile optimization guidelines from official Docker documentation. This skill should be used when writing, reviewing, or refactoring Dockerfiles to ensure optimal build time, image size, security, and robustness. Triggers on tasks involving Dockerfile creation, Docker image builds, container optimization, multi-stage builds, build cache, or Docker security hardening.

Emil Kowalski's animation best practices for web interfaces. Use when writing, reviewing, or implementing animations in React, CSS, or Framer Motion. Triggers on tasks involving transitions, easing, gestures, toasts, drawers, or motion.

Measures whether Claude uses an MCP server's tools correctly — tests tool selection accuracy, analyzes schema quality, and iteratively optimizes descriptions. Triggers when the user asks to "evaluate MCP tools", "test tool selection", "improve tool descriptions", "check MCP schema quality", or "eval my MCP server". Companion to build-mcp-server.

Library-documentation lookup methodology — API behavior, version-specific changes, idiomatic usage, or why production diverges from docs — independent of which library. Distills the generic navigation moves shared across libraries — classify the question before searching (changelog vs API reference vs idiom vs known-bug), check llms.txt before scraping HTML, pin to the user's version before reading reference pages, read changelog first for "did X change" questions, treat examples/ dirs as truth for idioms, and fall back to GitHub issues / status page / Discord when docs match but reality doesn't. Per-library topography lives in the shared /knowledge/libraries/ graph as thin reference data, alongside code-distill's section. Triggers on "where in <library> docs", "look this up in <library>", "did <library> change X", "docs say X but code does Y", and any prompt where the next move is to consult a library's official documentation.

Discovers business domains in a Swift codebase by tracing what users can DO — not by reading folder names or architecture docs. Maps each domain's vertical slice (Types → Config → Repo → Service → Runtime → UI), identifies providers (external SDK bridges), and separates cross-cutting concerns. Produces a domain map that drives all downstream decisions: folder structure, SPM targets, enforcement specs, migration plans. Use this skill whenever the user wants to understand their codebase domains, find what's cross-cutting vs domain-specific, restructure a Swift project, figure out where code belongs, or map a product's capabilities to architectural boundaries. Triggers on "what are my domains", "where does this belong", "map this codebase", "what's cross-cutting", "organize this project", "is this a domain or infra", "restructure this", "architecture review", or any request to understand the business domain structure of a Swift codebase.

Drizzle ORM against PostgreSQL inside a Next.js App Router app. Covers client construction (globalThis singleton across HMR, serverless pool sizing, `prepare:false` behind PgBouncer/Supavisor, driver choice when you need interactive transactions, `server-only`), reads in Server Components under Next.js 16 Cache Components (`use cache` superseding `unstable_cache` and the `revalidate`/`dynamic` segment configs, Suspense boundaries, React `cache()` dedupe), Server Actions (authorization inside the action, `updateTag` vs `revalidateTag`, `after()`), Postgres schema types (timestamptz, identity vs serial, jsonb, numeric-as-string, bigint modes), drizzle-kit migrations (generate vs push, CONCURRENTLY outside the migrator, NOT VALID constraints, rename prompts), transactions and pooled connections, and Postgres query traps (keyset pagination, count cost, driver-dependent `db.execute()` shape, NOT IN nulls, prepared statements). Use when writing or reviewing Drizzle + Postgres code in Next.js.

Drizzle ORM targeting SQLite (better-sqlite3, libsql/Turso, bun:sqlite, Cloudflare D1, expo-sqlite, op-sqlite). Covers schema definition (column modes, primary keys, foreign keys, indexes), drizzle-kit migrations (generate vs push, renames, custom SQL), the query builder (selects, upserts, returning, EXPLAIN), the relational query builder (relations(), `with`, partial columns), transactions and `db.batch()`, prepared statements with `sql.placeholder()`, connection pragmas (WAL, foreign_keys, busy_timeout), and Drizzle type inference (`$inferSelect`, `$inferInsert`, `$type<>`, drizzle-zod). Use when writing, reviewing, or refactoring Drizzle code for SQLite. Trigger even if the user doesn't say "performance" — schema/migration choices made now are expensive to reverse later, and SQLite-specific traps (single-writer model, no native booleans/dates, ALTER TABLE limits, FK pragma off by default) catch teams who reach for Drizzle without reading the SQLite docs.

Scaffolds Drizzle ORM + SQLite boilerplate — a new `drizzle.config.ts`, a singleton client with the right pragmas, per-table schema files with explicit primary keys/indexed foreign keys/relations()/inferred types, per-table repository modules with `.returning()` + `inArray()` + `.onConflictDoUpdate()`, or drizzle-zod validators. Produces convention-enforced templates for three drivers (better-sqlite3, libsql/Turso, bun:sqlite). Trigger even when the user doesn't say "scaffold" — phrases like "add a table for X", "set up Drizzle in this project", "wire up SQLite", "create a CRUD module for X", or "bootstrap the DB layer" should pull this in. Pairs with the `drizzle-sqlite` skill, which covers the 45 rules these templates encode — read it when an exception is required.

Developer-experience friction auditing and fixing — slow onboarding, repeated manual setup steps, missing bootstrap/reset/seed scripts, undiscoverable conventions. Audits the repo, scores findings, scaffolds canonical fixes (bootstrap.sh, reset.sh, seed.sh, AGENTS.md, task-runner entries), then verifies the harness end-to-end in a scratch worktree against a 60-second time-to-first-commit target. Triggers on phrases like "audit dx", "fix dev friction", "time to first commit", "set up the harness", "I keep doing X manually", "every time I reset the db I have to...", and on new-repo bootstrapping. Even if the user doesn't say "DX" — if they describe a repeated manual chore in their dev loop, this skill applies.

Effect-TS library usage in TypeScript — Effect.gen generators, Schema.Struct/Schema.Class definitions, Layer/Context.Tag/Service patterns, Effect.pipe pipelines, Data.TaggedError/Data.Class error types, Ref/Queue/PubSub/Deferred concurrency primitives, Match module, Config providers, Scope/Exit/Cause/Runtime patterns, or any code using Effect's typed error channel (E parameter). Trigger when writing, reviewing, debugging, or refactoring TypeScript code that uses Effect — when you see imports from `effect`, `effect/*`, or any `@effect/*` scoped package (schema, platform, sql, opentelemetry, cli, cluster, rpc, vitest). Also trigger when the user asks about Effect patterns, migration from Promises/fp-ts/neverthrow to Effect, or how to structure an Effect application. Do NOT trigger for React's useEffect, Redux side effects, or general English usage of "effect" unless the context clearly involves the Effect-TS library.

Use this skill when building an Elixir macro or a declarative DSL — a schema/spec/route/workflow language in the shape of Ecto.Schema, Absinthe, Plug.Router, or ExUnit — with quote/unquote, __using__, module attributes, and @before_compile. It corrects the wrong defaults a model makes once it commits to metaprogramming — putting logic inside the quote block, re-evaluating unquoted expressions, forgetting Macro.escape, fighting hygiene with var!, generating code ad hoc instead of accumulating declarations, and validating the DSL at runtime rather than at compile time. Applies to writing or reviewing macro/DSL code. NOT for deciding whether to use a macro at all — that gate belongs to staff-level-elixir and adversarial-elixir, which say prefer plain functions.

Manage the lifecycle of ExecPlans — self-contained, living specifications for multi-step work. Creates plans in the correct format, enforces living section updates, and handles the active → completed transition. Use for any work expected to take more than one session or touching more than 3 files. Triggers: "create a plan", "write a plan", "start plan", "continue plan", "resume plan", "finish plan", "complete plan", multi-step features, refactors, or tasks spanning sessions.

Expo React Native performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Expo React Native code to ensure optimal performance patterns. Triggers on tasks involving React Native components, navigation, lists, images, animations, bundle optimization, or mobile performance improvements.

Airbnb-DLS-aligned design system engineering for Expo / React Native apps targeting both web and native iOS, built on Unistyles v3, Reanimated, Skia, and FlashList. Use whenever building, reviewing, or refactoring shared UI — design tokens, theming, variant-driven component APIs, typography, spacing, cross-platform web/iOS parity, native-feel performance, or complex surfaces like calendars and drawing canvases (examples use a clinic app). Covers token architecture, theming, component API contracts (variants over style props), web/iOS parity (Unistyles `_web` hover/focus/cursor, Platform splits, one shared theme), the Unistyles styling engine, and governance. Trigger even when the user does not say "design system" but is creating or changing reusable React Native components, tokens, theme code, or making a component behave natively on both web and iOS. Teaches how to BUILD the design system; pair with expo-react-native-coder for features and expo-ios-hig for iOS native-feel decisions.

Scaffolds Expo / React Native design system components that obey the expo-design-system rules by construction — a variant-driven pressable primitive, a slot-based card surface, a typed text primitive, a labeled form field, a FlashList entity screen, a theme token group, and a Storybook variant catalog. Generated code uses Unistyles v3 variants instead of style props, ref-as-prop, design tokens, built-in accessibility, and web/iOS parity (`_web` hover/focus/cursor on interactive primitives), so it follows expo-design-system without rework. Output is TSX/TS using react-native-unistyles. Trigger whenever the user wants to create, add, generate, or scaffold a new shared UI component, primitive, design token group, or screen for the clinic mobile app — even if they don't mention the design system.

Expo / React Native iOS interface conforming to Apple Human Interface Guidelines (iOS 26) — native navigation (Expo Router native stack and tabs), platform controls (Alert, ActionSheetIOS, SF Symbols via expo-symbols), safe areas, dark mode, Dynamic Type, haptics, Liquid Glass, accessibility, and 60fps motion. Covers architecture and styling decisions made in React Native/TypeScript; for the @expo/ui SwiftUI component API use the expo-ui skill, and for native Swift use ios-hig. Trigger when building, reviewing, or refactoring such an app — even when the user does not say "HIG" or "native feel" but is writing TSX screens, navigation, lists, forms, or styling for iOS in Expo.

Static scan of an Expo / React Native iOS app for non-native patterns — Material component kits, JS navigators instead of native stack/tabs, PanResponder, disabled font scaling, hardcoded hex colors, unvirtualized lists — reports each violation with file:line and a link to the rule. Trigger when the user asks to check, lint, audit, or review an Expo iOS app for HIG compliance or "native feel," or before shipping/merging Expo iOS UI. Pairs with the expo-ios-hig rules skill (it checks what that skill teaches) and the expo-ios-screen-scaffolder.

Scaffolds Expo (React Native) iOS screens that follow Apple Human Interface Guidelines by construction — list, detail, form, modal sheet, native tabs layout, and settings screens, each wired with native navigation, FlashList, safe-area insets, semantic colors, SF Symbols, haptics, and empty/loading states. Generated code follows the expo-ios-hig rules so it passes expo-ios-hig-verify without rework. Each screen is TSX for Expo Router, not Swift. Trigger whenever the user wants to create, add, generate, or scaffold a new Expo screen, route, tab layout, or form for iOS — even if they don't mention HIG.

Comprehensive Expo React Native feature development guide. This skill should be used when building mobile app screens, navigation, data fetching, authentication, deep linking, or native UX patterns with Expo. Triggers on tasks involving Expo Router, React Native components, mobile forms, or app configuration.

Expo React Native performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Expo React Native code to ensure optimal performance patterns. Triggers on tasks involving React Native components, lists, animations, images, or performance improvements.

Library reference for @expo/ui SwiftUI components on iOS — covers Host boundaries, modifier composition, iOS 26 Liquid Glass and Human Interface Guidelines composition rules, layout/input/navigation/display catalogues, and ObservableState patterns. Use this skill whenever writing or reviewing React Native code that imports from @expo/ui/swift-ui or @expo/ui/swift-ui/modifiers — including new Expo apps adopting native SwiftUI views, migrations from React Native primitives to expo-ui, and code targeting iOS 26 features (Liquid Glass, GlassEffectContainer, sheet detents). Trigger even if the user does not explicitly mention "expo-ui" but is writing iOS-targeted Expo UI code that should bridge to SwiftUI.

React feature-based architecture guidelines for scalable applications. This skill should be used when writing, reviewing, or refactoring React code to ensure proper feature organization. When invoked on a project, the agent produces a concrete target-architecture blueprint at docs/architecture/FEATURE-ARCH-TARGET.md showing the desired directory tree, per-feature public APIs, import-boundary matrix, and a numbered migration plan. Triggers on tasks involving project structure, feature organization, module boundaries, cross-feature imports, data fetching patterns, or component composition.

Adversarial pass/fail review gate that enforces the feature-arch skill's React feature-based architecture rules. Use when a diff, branch, PR, or src/ tree must be judged for architecture conformance before merge — feature folder structure, import boundaries, cross-feature isolation, data-fetching, state, testing, and naming rules. A single blind reviewer renders per-rule PASS/FAIL verdicts with cited evidence, and the reviewer's structured output is the verdict fail-closed. Use the feature-arch skill itself to design or migrate an architecture; use this gate to judge whether work conforms to it.

Fog of war, field of view, line of sight, and tile visibility in JavaScript or TypeScript games — roguelikes, RTS, top-down, or strategy maps. Covers efficient FOV algorithms (recursive and symmetric shadowcasting, DDA raycasting, 2D visibility polygons), update scheduling and multi-viewer reference counting, typed-array and bitset state, canvas and WebGL fog rendering, memory and large-map scaling, hot-loop geometry math, and visibility correctness. Trigger when implementing, reviewing, or optimizing such code — even when the user only says "fog of war", "reveal the map", "what can this unit see", "shadowcasting", or "visibility grid" without mentioning performance — the naive raycast-per-cell-every-frame approach is usually what needs replacing. Prefer these patterns when writing new visibility code or refactoring slow or buggy fog.

Framer Motion performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring React animations with Framer Motion to ensure optimal performance patterns. Triggers on tasks involving motion components, animations, gestures, layout transitions, scroll-linked effects, and SVG animations.

Geohash encoding/decoding in TypeScript or Rust — bit interleaving, the base32 alphabet, precision and cell geometry, neighbour/adjacency computation, proximity and bounding-box queries, and geohash-backed spatial indexing. Also covers the "codebase as a navigable 2D map" pattern — projecting a codebase into a coordinate plane, geohashing it so prefixes become business-domain regions, and navigating it like Google Maps (zoom, tiles, level-of-detail, clustering, deep links). Trigger when implementing, reviewing, or debugging geohash work — even when the user does not say "geohash" but the work involves spatial hashing, Morton/Z-order codes, proximity search on lat/lon, or mapping and visualising code structure spatially. Contains 42 impact-ordered rules with TypeScript and Rust examples.

Write Go CLIs that manage processes and workloads — daemons, supervisors, job runners, deploy tools, anything that spawns or controls other processes. Use when writing, reviewing, or refactoring Go that handles OS signals, runs children via os/exec, fans out concurrent work, threads context for cancellation, returns exit codes, or builds a flag/cobra command surface. Covers the footguns the standard library makes easy to hit — SIGTERM vs Ctrl-C, SIGKILL-by-default children, orphaned process groups, pipe deadlocks, goroutine leaks, zombie reaping, and os.FindProcess lying about liveness. Triggers whenever the task touches process lifecycle, graceful shutdown, subprocess control, or concurrent workload supervision in Go — even if not named.

Graph database schema design and data modeling expert. Use when designing, reviewing, or refactoring graph database schemas (Neo4j, Memgraph, Neptune, etc.). Triggers on graph modeling, node/relationship design, Cypher schema, property graph design, knowledge graph modeling, or when translating a domain into a graph structure. Focuses primarily on data modeling correctness — understanding the user's goal and translating it into the right graph structure — with performance as a secondary concern.

Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.

User-facing copy — UI strings, documentation, marketing, release notes, error messages, onboarding flows, blog posts, or anything that a person will read. Distilled from the Apple Style Guide (June 2025): voice and tone (active voice, present tense, second person, no jargon), punctuation, capitalization, hyphenation rules for compound modifiers, when to spell out numbers, units of measure, UI vocabulary (button vs control vs sheet vs popover), Apple product and feature naming, technical notation for developer docs, and international style for localization. Trigger when writing, editing, or reviewing copy — even if the user doesn't explicitly mention "style guide" or "copywriting," any time the deliverable is human-readable text the rules here apply. Especially valuable when the user is writing instructions, naming UI elements, or deciding how to format a number, date, or unit.

Remove signs of AI-generated writing from text (formerly human-writing). Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Implementation guide for the 22 Gang of Four design patterns in TypeScript, distilled from refactoring.guru. Use this skill when writing, refactoring, or reviewing TypeScript that exhibits a pattern-shaped problem — class-explosion from inheritance, conditionals switching on type, tight coupling to concrete classes, tree-shaped models, runtime algorithm selection, undo/redo, snapshot-and-restore, state-dependent behavior, subscriber notification, or hiding subsystem complexity. Each pattern entry includes intent, problem, solution, applicability (when to use AND when NOT to use), a runnable TypeScript example, implementation steps, pros/cons, and relations to sibling patterns. Trigger even when no pattern is named — cues like "class getting unwieldy," "giant switch," "swap implementations at runtime," "combinatorial subclasses," "need undo," or "traverse a tree" are pattern-shaped. Covers all 5 Creational, 7 Structural, and 10 Behavioral GoF patterns.

Implementation guide for the 22 Gang of Four design patterns in idiomatic modern Python (3.10+), distilled from refactoring.guru. Use when writing, refactoring, or reviewing Python with a pattern-shaped problem — class-explosion from inheritance, conditionals switching on type, tight coupling to concrete classes, tree-shaped models, runtime algorithm selection, undo/redo, state-dependent behavior, or hiding subsystem complexity. Each entry leads with the Pythonic form (functions, dataclasses, Protocol, singledispatch, match, generators, copy/replace) and falls back to the class-based GoF structure only when identity, state, or dispatch require it. Includes intent, applicability (when to use AND when NOT to), a runnable example, steps, pros/cons, and relations. Trigger even when no pattern is named — cues like "too many constructor params," "giant if/elif," "swap behavior at runtime," "need undo," or "walk a tree" are pattern-shaped. Covers all 5 Creational, 7 Structural, and 10 Behavioral GoF patterns.

TypeScript's functional answers to the 22 Gang of Four classes — factory functions (Factory Method, Abstract Factory, Prototype, Memento), module-scope singletons, fluent immutable builders, wrapper functions (Adapter, Facade), native Proxy, WeakMap caches (Flyweight), discriminated unions with exhaustive match (State, Visitor, Composite), event emitters and signals (Mediator, Observer), pipelines and composition (CoR, Decorator), stream methods (Iterator), closures-as-commands, higher-order strategies, lambda placement. Use when reviewing TypeScript that has a class-shaped problem the GoF catalog solves with a hierarchy but where idiomatic TS reaches for a function, a tagged union, or a data structure. Each rule names the GoF pattern(s) it replaces and when the class form still wins. Trigger on "factory class", "singleton getInstance", "state machine class", "observer pattern", "AST visitor", "where do I put this lambda". Sibling to implementation-design-patterns.

Inngest event-driven functions in a Next.js (App Router) project — creating the client, the /api/inngest route handler, typed events with Zod schemas, durable event-triggered functions, scheduled cron functions, or fan-out orchestrators. Trigger when scaffolding Inngest patterns — and even if the user just says "add a background job", "process this async", "schedule X every hour", or "send an event when Y happens" in a Next.js codebase. Also trigger when reviewing existing Inngest code to enforce event-naming, function-id stability, and step-idempotency conventions.

Legacy interoperability skill for Storyboard and Interface Builder maintenance in iOS 26 / Swift 6.2 clinic codebases. Use only for migration or maintenance of existing storyboard screens; not for new SwiftUI clinic feature development. Triggers on Auto Layout, segues, size classes, IB accessibility, storyboard merge conflicts, and storyboard-to-SwiftUI migration tasks.

Designs iOS 18+ SwiftUI experiences with real taste — starting from user goals, not pixels. Use this skill whenever the user asks you to build SwiftUI views, screens, or experiences. Trigger when the user says "build a settings screen", "create a detail view", "design this properly", "I want this to feel like a native app", or any SwiftUI UI task. Also trigger when reviewing SwiftUI code for design quality, or when the user says the output "looks like a demo" or "feels generic." When building any user-facing SwiftUI view, lean toward triggering this skill.

Testing practices for iOS 26 / Swift 6.2 clinic modular MVVM-C applications. Covers unit/UI/snapshot testing, protocol-based mocks, async actor isolation, and dependency-injected test architecture aligned with Domain protocols, App-target composition, and Data-owned I/O boundaries. Use when writing, reviewing, or refactoring tests for ios-* and swift-* clinic modules.

Principal-level SwiftUI UI review and refactoring patterns for iOS 26 / Swift 6.2 clinic-architecture apps, grounded in Rams, Segall, and Edson principles. Use when auditing or improving existing SwiftUI screens, transitions, animations, and visual systems while preserving brand identity and respecting clinic Domain/Data/App boundaries.

Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mmap, async), data formats (Parquet vs CSV vs JSON, predicate pushdown), chunking & batching, spill-to-disk (external merge sort, DuckDB/Polars), pipelining (bounded queues, backpressure, checkpointing), codec selection (zstd/lz4/gzip), concurrency for I/O-bound workloads (asyncio, threads, prefetch), and observability (iowait vs CPU%, rows/sec, py-spy/strace). Trigger on "process a large file", "stream this", "out-of-core", "OOM kill", "this is slow", or code with `pd.read_csv` of multi-GB files, `requests.get(...).content` on big bodies, `BytesIO` on unbounded inputs, per-row INSERTs, sequential `requests.get` loops, falling `tqdm` rates — even if I/O or memory isn't mentioned. Complement to computer-science-algorithms.

Clinic-architecture-aligned iOS animation craft guidelines for SwiftUI (iOS 26 / Swift 6.2) covering motion tokens, spring physics, gesture continuity, spatial transitions, micro-interactions, and accessibility. Enforces @Equatable on animated views and keeps animation state aligned with Domain/Data feature boundaries. Use when writing, reviewing, or refactoring SwiftUI animation code under the clinic modular MVVM-C architecture.

Crash-hunter skill for iOS 26 / Swift 6.2 clinic-architecture codebases that finds and fixes concurrency, memory, and I/O bugs using TDD. Covers data races, actor isolation, retain cycles, SwiftData context misuse, and sync-related failures in Domain/Data/App boundaries. Use when debugging crashes or hard-to-reproduce failures in ios-* and swift-* clinic modules.

SwiftUI interface implementation patterns aligned with the iOS 26 / Swift 6.2 clinic modular MVVM-C architecture, grounded in Creative Selection and Design Like Apple principles. Use when building new SwiftUI views/screens while respecting Domain/Data boundaries, App-target route-shell navigation, and production-grade accessibility/interaction standards.

Clinic-architecture-aligned iOS design system engineering for SwiftUI (iOS 26 / Swift 6.2) covering token architecture, color/typography/spacing systems, component style libraries, asset governance, and theming. Enforces @Equatable on views and keeps design-system usage compatible with Feature-to-Domain+DesignSystem boundaries. Use when building or refactoring DesignSystem infrastructure for the clinic modular MVVM-C stack.

Apple Human Interface Guidelines for iOS 26 / Swift 6.2 clinic-architecture apps. Covers navigation, interaction design, accessibility, feedback states, UX patterns, and visual design for SwiftUI implementations that follow App-target coordinators/route shells and Domain/Data boundaries. Use when designing or reviewing HIG-compliant experiences in the clinic modular MVVM-C stack.

Opinionated SwiftUI navigation enforcement for iOS 26 / Swift 6.2 clinic modular MVVM-C apps. Enforces Domain coordinator protocols, App-target `DependencyContainer` + concrete coordinators + route shells, `NavigationPath` ownership, coordinator-owned modal state, deep-link/state-restoration readiness, and stale-while-revalidate/optimistic queued flow compatibility. Use when designing or refactoring clinic navigation flows.

Turn a rough idea for a language into a complete, implementable specification — a DSL, query, config/data, template, or protocol language — by interviewing the author dimension by dimension until another developer could build a conforming implementation from the document alone. It grills for the decisions authors skip: lexical rules (whitespace, case, comments, literals), grammar with precedence and ambiguity resolution, a semantic/type model, validation rules with counter-examples, execution algorithms and the error model, the output/serialization format, and RFC 2119 conformance. The completeness bar and formal notation (lexical vs syntactic grammar, function-style algorithms) are distilled from the GraphQL specification. Trigger on "spec out my language", "design a DSL / query language", "write a language or grammar spec", "formalize this syntax", or when someone has a language idea that needs to become an implementable spec — even if they only say "spec" or "grammar".

Methodology for starting a new library-reference distillation skill — one that turns an external library (nuqs, zod, framer-motion, msw, react-hook-form, emilkowal-animations) into an idiomatic-usage rule pack — or evolving one against a new upstream release. Distills the conventions empirically shared across shipped library-ref skills in this repo — the source-priority ladder (docs → blog/changelog → issues → types → examples), version pinning that inverts with API velocity, the universal 4-tier category ladder (CRITICAL setup → HIGH isolation → MEDIUM composition → LOW edge cases), the 4-slot When-to-Apply template, the failure-gap exemplar heuristic (privilege production lessons over API restatement), and metadata.references[] as cite-set checksum. Triggers on "I want to write a skill for library X", "refresh against new upstream", "where should I source rules from", "what categories should this skill have", and on /dev-skill:new for a library-reference distillation.

Mapping out an unfamiliar codebase via NLP and graph algorithms — 40 algorithms across topic modelling, semantic embeddings, code graphs, repository mining, clone detection, IR-based bug localization, identifier linguistics, and complexity metrics. Trigger when hunting bugs across many files, scoping a new feature, identifying domain entities, or analyzing commit history — even if the user doesn't explicitly mention algorithms — apply when they ask "where does X live in this codebase?", "what is this codebase about?", "find duplicated logic", "what changed recently?", "who owns this code?", or "is this function risky?".

Personalisation and recommendation systems for a two-sided trust marketplace built on AWS Personalize — event tracking, dataset and schema design, two-sided matching, cold start, feedback loops, bias control, recipe selection, serving-time re-ranking, observability, and a diagnostic playbook for existing systems. Trigger when designing, building, debugging, reviewing, or improving such a system — and even when the user does not explicitly mention "AWS Personalize" but is working on recommendations, ranking, search, homepage personalisation, or anything that matches seekers and providers across a trust-based catalog.

Pre-member journey of a two-sided trust marketplace — from anonymous landing through onboarding, registration, and the paid-membership paywall. Covers anonymous signal inference, what pet owners specifically need to validate before paying (safety, availability, competence, effort, local cost comparison), what pet sitters specifically need to validate (opportunity, first-stay path, daily commitment, hidden costs), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Triggers on tasks involving visitor-to-member conversion, anonymous personalisation, onboarding flow design, paywall timing, pre-member ranking, or any question about what a pet owner or pet sitter needs to see before paying. Use this skill BEFORE marketplace-personalisation and marketplace-search-recsys-planning.

Feature engineering for marketplace recommenders — what to extract from raw marketplace assets (listing photos, owner-entered listing metadata, sitter wizard responses) to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders. Covers asset auditing, first-principles feature decomposition, vision-feature extraction (CLIP, room-type, amenities, aesthetics), listing text and metadata encoding, sitter wizard design, derived-composition patterns for i2i / u2i / u2u (ANN shelves, two-tower, mutual-fit), feature quality governance (training-serving parity, drift, PII), and incremental value proof (ablation A/B, kill reviews, feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.

Search and recommendation system planning for a two-sided trust marketplace built on OpenSearch — user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific.

Empirically validates a software metric before trusting or optimizing it — point it at any candidate metric (a command that takes a path and prints one number) plus a corpus, and it runs experiments that try to falsify each property a good metric must have. Checks determinism (same input, same number across runs and hash seeds), invariance to cosmetic edits (also an anti-gaming probe), monotonicity under construct-increasing edits, discrimination, robustness on edge inputs, near-linear tractability, and construct validity (convergent, discriminant vs LOC, predictive AUC, lift over a baseline). Trigger whenever someone proposes, reviews, tunes, or ships a metric, score, or index, asks "is this metric any good", suspects a score tracks LOC or jumps between runs, or builds a deterministic optimization target. It is the empirical companion to the deterministic-metric-design skill and is read-only.

Migrating a JavaScript codebase to TypeScript — converting .js files to .ts, adding types to existing JS, or tightening a loosely-typed TS project toward strict mode. Covers tsconfig and allowJs strategy, incremental strict-flag ratcheting (noImplicitAny, strictNullChecks, noUncheckedIndexedAccess), typing public surfaces, replacing `any` and unsafe casts with `unknown` and narrowing, validating runtime boundaries (JSON, env, API responses), converting CommonJS to ESM and prototypes to classes, and the build/CI changes a migration needs. Trigger even when the user only says "add types", "turn on strict mode", or "convert this file to TypeScript", and especially on a mixed JS/TS repo. Distinct from general TypeScript refactoring — this is the migration act itself, performed file by file while keeping the build green.

MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.

Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models. Walks seven phases with a developer who may have zero MLflow 3 experience — assess the codebase (scripted read-only audit), model the registry domain (per-environment model names, aliases, gates), stand up tracking per environment, restructure training code to MLflow 3 idioms, wire evaluation-gated promotion, serve and smoke-test, then run the ongoing MLOps loop. Use when asked to set up MLflow, migrate to MLflow 3, productionize model training and serving, or design a dev/staging/prod MLOps cycle. Pairs with the sibling mlflow-3 rule pack for every API decision.

Next.js 16 App Router performance, caching, server components, server actions, routing, and codebase-hygiene best practices — plus a category-major review/refactor algorithm with codebase-level (remove/dedup/reuse) findings. This skill should be used when writing Next.js 16 App Router code, configuring caching with 'use cache', building Server Components, setting up parallel/intercepting routes, configuring next.config.js OR proxy.ts, OR auditing/refactoring a Next.js codebase (single file or whole repo). This skill does NOT cover generic React 19 patterns (use react skill) or non-Next.js server rendering.

Next.js 16 bundle-size and build-time optimization — runs a data-driven iteration loop: measure baseline → analyze top offenders → apply ONE recipe → re-measure → verify nothing broke (build + types + tests + no regression) → commit or revert. Built for Next.js 16 with Turbopack default, and falls back to webpack-mode tooling when the project hasn't migrated yet. Triggers on phrases like "First Load JS is huge", "bundle size", "build takes too long", "page is slow to TTI", "reduce bundle", "tree-shake", or when the user shares output from `next experimental-analyze` / `@next/bundle-analyzer` — even if the user doesn't say "optimize" explicitly.

Next.js 16 App Router pages mixing static and dynamic content — Partial Prerendering (PPR) under the Cache Components model. Covers enabling it with cacheComponents (the removed experimental.ppr / experimental_ppr flags), the dynamic-by-default rendering inversion, the Suspense static-shell/dynamic-hole boundary, the 'use cache' directive (automatic keys, cacheLife/cacheTag, children/action pass-through, runtime values as props, serverless durability), async runtime APIs and connection() for non-determinism, page composition from a single hole to parallel dashboards to streaming a Promise into a Client Component with use(), and forms/wizards with updateTag read-your-writes and Activity state preservation. Triggers on PPR, cacheComponents, 'use cache', Suspense streaming, partial prerendering, or static-shell work even when not named explicitly.

nginx C module debugging guidelines based on the official nginx development guide. This skill should be used when debugging nginx C module crashes, memory bugs, request flow issues, or production problems. Triggers on tasks involving segfault analysis, coredump debugging, GDB inspection, memory leak detection, request phase tracing, AddressSanitizer setup, or nginx module troubleshooting.

nginx C module directive design guidelines for creating admin-friendly configuration interfaces. This skill should be used when designing nginx module directives — deciding what to expose vs hardcode, naming conventions, scope placement, default values, variable design, and validation patterns. Triggers on tasks involving ngx_command_t design, directive naming, configuration API design, nginx module public interface, or directive deprecation.

nginx C module performance optimization and reliability guidelines based on the official nginx development guide. This skill should be used when optimizing nginx C modules for throughput, latency, memory efficiency, and operational resilience. Triggers on tasks involving buffer optimization, connection tuning, shared memory contention, error recovery, timeout strategy, caching implementation, worker process tuning, or logging performance in nginx C modules.

nginx C module development guidelines based on the official nginx development guide. This skill should be used when writing, reviewing, or refactoring nginx C modules to ensure correct memory management, request lifecycle handling, and event-driven patterns. Triggers on tasks involving nginx module development, ngx_http_module_t, handler/filter/upstream implementation, pool allocation, or nginx configuration directives.

nuqs (type-safe URL query state) best practices for Next.js and other React frameworks. This skill should be used when writing, reviewing, or refactoring code that uses nuqs for URL state management. Triggers on tasks involving useQueryState, useQueryStates, search params, URL state, query parameters, nuqs parsers, limitUrlUpdates, Standard Schema, NuqsAdapter, or Next.js routing with state.

Migrates a nuqs codebase off pre-v2.5 patterns — deprecated `throttleMs`, hand-rolled setTimeout/useState debounce around `useQueryState`, unchecked `parseAsJson` casts, unversioned `nuqs/adapters/react-router` imports, or `ParserBuilder<T>` type references. Scans the repo, produces a dry-run report, asks for confirmation, applies AST codemods, then runs `tsc --noEmit` + the user's lint as a gate. Trigger even if the user only mentions "upgrade nuqs" or "migrate to nuqs v2.5+" without naming the specific patterns — they almost always exist in pre-v2.5 codebases.

Scaffolds URL-state filters for a Next.js page — typed `searchParams.ts` parser map and a `<Filters />` client component backed by `useQueryStates`. From a single JSON spec, generates four files in lockstep — client parser map, server loader/cache/serializer, client component, and Vitest test — all sharing the same parser definitions per the nuqs Standard Schema pattern. Trigger even when the user only says "add filters to /search" or "I need a typed query string for this page" — both are exactly this skill's job.

OpenAI Codex Rust coding patterns distilled from the codex-rs workspace. Use this skill whenever writing, reviewing, or refactoring Rust code — especially for async agents, CLI tools, sandboxing, secret handling, Ratatui TUIs, JSON-RPC protocols, tokio-based services, or any codebase that needs defensive panic discipline. Trigger even when the user does not explicitly mention Codex, because the patterns generalize to any production Rust workspace. Covers async cancellation, error enum design, process sandboxing, DNS-rebinding defense, credential hardening (zeroize/mlock/ctor), Cargo workspace architecture, wiremock-based fakes, insta snapshot testing, OpenTelemetry tracing, and Ratatui rendering.

Write and refactor TypeScript code in repos that use Effect-TS services, Effect Schema, event-sourced persistence, and barrel-module architecture. Use this skill when implementing features, fixing bugs, writing tests, or refactoring in opencode or any TypeScript codebase built on the same stack (Effect DI via Context.Service, Drizzle ORM, Hono routes, Bun runtime). Triggers on tasks involving Effect services, Context.Service / Layer modules, Effect Schema definitions, SyncEvent patterns, tool implementations, test writing, or code review in Effect-based TypeScript projects.

Portuguese personal income tax (IRS / Código do IRS) and Modelo 3 filing — determining tax residency (Art. 16), classifying income by category (rental/Anexo F, capital gains/Anexo G, employment/Anexo A, self-employment/Anexo B, pensions, foreign income/Anexo J), choosing the right annexes, applying current Art. 72 autonomous rates, and meeting Portal das Finanças deadlines. Covers residents and non-residents, including landlords with Portuguese-source rental income and double-taxation relief. Portugal-specific; not a substitute for a contabilista certificado.

Pulumi infrastructure as code performance and reliability guidelines. This skill should be used when writing, reviewing, or refactoring Pulumi code to ensure optimal deployment performance and infrastructure reliability. Triggers on tasks involving Pulumi stacks, components, state management, secrets configuration, resource lifecycle options, or CI/CD automation.

Python 3.11+ performance optimization guidelines (formerly python-311). This skill should be used when writing, reviewing, or refactoring Python code to ensure optimal performance patterns. Triggers on tasks involving asyncio, data structures, memory management, concurrency, loops, strings, or Python idioms.

Ruby on Rails performance and maintainability optimization guidelines for building backend APIs and frontend web applications. This skill should be used when writing, reviewing, or refactoring Ruby on Rails code to ensure optimal patterns for controllers, models, ActiveRecord queries, caching, views, API design, security, and background jobs. Triggers on tasks involving Rails controllers, ActiveRecord queries, migrations, Turbo/Hotwire, API endpoints, background jobs, or Rails performance improvements.

Ruby on Rails Hotwire best practices for building interactive applications with Turbo Drive, Turbo Frames, Turbo Streams, Turbo 8 morphing, and Stimulus controllers. This skill should be used when writing, reviewing, or refactoring Hotwire-powered Rails code to ensure optimal patterns for navigation, partial page updates, real-time broadcasting, morphing, Stimulus controller design, error handling, and progressive enhancement. Triggers on tasks involving Turbo Frames, Turbo Streams, Turbo Drive, broadcasts, morphing, Stimulus controllers, ActionCable, turbo_stream_from, turbo_frame_tag, data-controller, data-action, or Hotwire performance. Complementary to rails-dev, rails-testing, rails-design-system, ruby-optimise, and ruby-refactor skills.

Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.

Caching strategies in front of AWS OpenSearch (Elasticsearch) or AWS Personalize — search, recommenders, multi-recommender pages, anon vs logged-in traffic. Covers ROI decision (TPS/minProvisionedTPS, Zipf, amplification), key design (canonicalisation, cohort vs user, solution-version pinning, bucketing), personalisation boundary (anon/logged split, fan-out coalescing), strategies (cache-aside, refresh-ahead, write-through, batch precompute, L1+L2), TTL (volatility, soft/hard, jitter, event-driven invalidation), stampede protection (single-flight, XFetch, stale-while-revalidate, circuit breaker), observability (hit-rate, cost-per-1k, cardinality drift, log-replay), defensive caching (negative, Bloom filter), and tier composition (LRU, ElastiCache Redis, CloudFront, OpenSearch request/filter cache). Triggers on cache hit rate, Personalize throttling, stampede, single-flight, L1/L2, ElastiCache sizing. Complements opensearch-function-scoring-algorithms.

Opinionated, backend-agnostic Next.js 16 (App Router) architecture — authorization at the data layer, server-side loading with cache()+Promise.all, mutations through next-safe-action + typed route handlers, client/server boundaries ('use client' at leaves + TanStack Query), forms with RHF + Zod, UI via shadcn/ui + Tailwind + Base UI + next-intl, request handling in proxy.ts (Next.js 16's renamed middleware), and a Turbo monorepo of @app/* packages confining the backend behind one data-access package. Examples use Supabase but every rule states the transferable principle. Use when writing, reviewing, or refactoring Next.js 16 code. Trigger on server actions, route handlers, RSC vs 'use client' placement, TanStack Query, RHF/Zod, proxy.ts, or monorepo package layout — even when the user doesn't say 'patterns' or 'best practices'.

Orval OpenAPI TypeScript client generation best practices. This skill should be used when configuring Orval, generating TypeScript clients from OpenAPI specs, setting up React Query/SWR hooks, creating custom mutators, or writing MSW mocks. Triggers on tasks involving orval.config.ts, OpenAPI codegen, API client setup, or mock generation.

Drafting Python Enhancement Proposals (PEPs) — proposing a Python language feature, a standard library change, an interoperability standard, or an informational/process document for the Python community. Covers all three PEP types (Standards Track, Informational, Process): choosing the right type, scaffolding a valid reStructuredText pep-NNNN file, filling each required section to the acceptance bar, linting the headers, and navigating the sponsor / submission / Steering Council review process. Trigger on "write a PEP", "draft a PEP", "propose a Python feature", "create a Python Enhancement Proposal", "PEP template", or when someone is preparing a proposal for the Python Discourse or python-dev. Use it even if the user only says "PEP" or doesn't mention reStructuredText — the headers, sections, and process are exactly what trips PEP authors up.

Cognitive moves for collapsing complexity — reframe, clarify, reduce, decompose, invert, constrain, transfer, generalize, audit — distilled from the working method of mathematicians, physicists, and software engineers known for turning hard problems into simple solutions (Pólya, Feynman, Hamming, Brooks, Knuth, Dijkstra, Lamport, Tao, Grothendieck, Munger, Hofstadter). Use when stuck on a complex problem, when a proposed design feels overengineered, when reviewing code that has accreted accidental complexity, or when the user wants an elegant solution to a hard engineering or product problem. Triggers on phrases like "this feels too complicated", "we are going in circles", "there must be a simpler way", "interview me about this plan", "find the underlying problem", and on stuck-state moments where forward search has run out and the agent needs a different angle.

Compose new Rails backend pages and refactor existing Rails UI to use premium blocks from templates/application-ui. Use when requests mention ERB views, Rails partials, admin/dashboard screens, Tailwind UI assembly, or replacing custom markup with existing premium blocks while preserving behavior, accessibility, and Turbo/Stimulus hooks.

Ruby on Rails design system guidelines for building consistent, maintainable UI with minimal abstraction. This skill should be used when creating or refactoring Rails views, partials, components, form builders, helpers, Stimulus controllers, Turbo Frames, Turbo Streams, or design tokens. Triggers on tasks involving ERB partials, Turbo navigation, Turbo Streams, ViewComponent, Phlex, Tailwind design tokens, custom form builders, view helpers, Stimulus behaviors, Import Maps, Lookbook previews, or design system consistency audits.

Ruby on Rails testing best practices for writing effective, maintainable test suites with RSpec. This skill should be used when writing, reviewing, or refactoring Rails tests to ensure proper test design, data management, and coverage patterns. Triggers on tasks involving RSpec specs, model tests, request specs, system tests, factory definitions, Capybara interactions, Sidekiq job tests, or test suite optimization. Complementary to rails-dev, ruby-optimise, and ruby-refactor skills.

Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.

LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.

Write production-grade Rust code using a multi-pass approach. Design types first, then implement, then simplify, then verify with automated lint. Use this skill whenever writing new Rust functions, structs, modules, or features. Triggers on Rust implementation, new Rust code, Rust functions, Rust modules, error handling in Rust, async Rust, or type design in Rust.

Decision frameworks for Rust refactoring, simplification, module decomposition, and incremental migration. Use this skill when simplifying Rust code, splitting large files, removing dead abstractions, migrating types incrementally, or cleaning up feature flags. Triggers on Rust refactoring, simplification, module splitting, parameter cleanup, or incremental type migration.

Expert-level Rust testing — the "What Could Break?" framework, five transformations from superficial to expert tests, flake hunting protocol, intent-based assertions, naming conventions, and a mandatory self-review checklist. Triggers on writing Rust tests, designing test cases, improving test quality, or reviewing test coverage.

Same behaviour in fewer, clearer lines — covers the judgment gaps that linters cannot catch (reinvention, wrong frame, hidden duplication, derived state, procedural rebuilds, speculative generality, defensive excess, type-system underuse). Trigger when reviewing, refactoring, or simplifying code — and even when the user doesn't explicitly ask for "simplification" but is reviewing code, refactoring, or asking "is there a shorter way to write this?". Complements knip/eslint/ruff/tsc by focusing on the conceptual modelling layer those tools cannot see.

Search and inspect local iOS 26 SDK documentation and Swift interfaces, especially SwiftUI and SwiftUICore. Use when Codex needs current iOS 26 SwiftUI APIs, Liquid Glass components, view modifiers, availability, signatures, deprecations, symbol docs, or SDK-only facts and should not rely on memory or public web docs being available.

Search and inspect local macOS 26 SDK documentation and Swift interfaces, especially SwiftUI, SwiftUICore, and AppKit. Use when Codex needs current macOS 26 APIs, Liquid Glass components, window, scene, menu, toolbar, AppKit bridging, availability, signatures, deprecations, symbol docs, or SDK-only facts and should not rely on memory or public web docs being available.

React 19/19.2 modern patterns for concurrent rendering, Server Components, actions, ref-as-prop, document metadata, resource hints, hooks, and memoization — plus a category-major review/refactor algorithm with codebase-level (remove/dedup/reuse) findings. This skill should be used when writing React 19 components, using concurrent features, migrating from React 18, optimizing re-renders, OR auditing/refactoring a React codebase (single file or whole repo). This skill does NOT cover Next.js-specific features like App Router, next.config.js, or Next.js caching (use nextjs-16-app-router skill). For client-side form validation with React Hook Form, use react-hook-form skill.

Scaffolds React 19 / React 19.2 code in TypeScript — components, Server Component pages, client islands, form actions with useActionState, context providers, custom hooks, reducers, or document metadata + resource hints. Generates production-grade code that follows React 19 idioms (ref-as-prop, <Context value={...}>, useActionState, inline metadata, useSyncExternalStore) and refuses deprecated React 18 patterns (forwardRef, <Context.Provider>, useFormState, react-dom/test-utils). Trigger even when the user says "create a component", "new page", "add a form", "new hook", or "scaffold X" without explicitly mentioning React 19.

React data-fetching patterns at scale — recommender carousels, infinite feeds, pages with many parallel fetches, dashboards. Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser). Trigger when building, reviewing, or refactoring React components that fetch data — even if the user doesn't explicitly mention "performance" or "scale".

React Hook Form performance optimization for client-side form validation using useForm, useWatch, useController, useFieldArray, the subscribe() API, and the Watch / FormStateSubscribe / FieldArray render-prop components. Covers RHF 7.82 additions including resetDefaultValues() and the disabled field-array option. This skill should be used when building client-side controlled forms with React Hook Form library. This skill does NOT cover React 19 Server Actions, useActionState, or server-side form handling (use react-19 skill for those).

shadcn/ui component library best practices and patterns (formerly shadcn-ui). This skill should be used when writing, reviewing, or refactoring shadcn/ui components to ensure proper architecture, accessibility, and performance. Triggers on tasks involving Radix primitives, Tailwind styling, form validation with React Hook Form, data tables, theming, or component composition patterns.

Shell scripting best practices for writing safe, portable, and maintainable bash/sh scripts. Use when writing, reviewing, or refactoring shell scripts, Dockerfile RUN commands, Makefile recipes, CI pipeline scripts, cron jobs, or systemd ExecStart directives. Triggers on bash, sh, POSIX, ShellCheck, error handling, quoting, variables, set -euo pipefail.

Design and development best practices for Claude Code skills, MCP tools, and AI agent capabilities. Use when creating skills, writing SKILL.md files, designing tool descriptions, or optimizing triggers. Triggers on "create a skill", "skill template", "write skill instructions", SKILL.md, metadata.json, progressive disclosure, trigger optimization, MCP tool design, or skill testing. Does NOT cover specific frameworks or languages (use dedicated skills).

Tailwind CSS code refactoring patterns for v4 migration and anti-pattern cleanup. This skill should be used when refactoring Tailwind utility classes, migrating from v3 to v4, cleaning up deprecated utilities, consolidating verbose class patterns, or removing code smells — all without changing the visual output. Triggers on tasks involving Tailwind CSS cleanup, v4 migration, class refactoring, @apply removal, or utility modernization.

Audits a Next.js (App Router, 14/15+) codebase for React Hook Form anti-patterns — watch() at form root, Controller inlined in parent, async submit without try/catch, missing setError on server failures, RHF in non-"use client" files, RHF mixed with useActionState, schemas defined inside components, useFieldArray without field.id keys, register({ disabled }) for visual disabling, useFieldArray({ disabled }) that silently no-ops mutations, useEffect+reset(data) instead of the values prop. Read-only; emits a markdown report with file:line citations linking back to the companion `react-hook-form` distillation skill. Trigger when the user asks to audit/review/lint RHF usage, find form anti-patterns, or run a quality check on forms — even if they don't say "react-hook-form" by name; if they mention auditing forms in a Next.js project, use this skill.

React Native Elements UI component library best practices for performance, theming, and proper component usage. Use when building React Native apps with RNE, configuring themes, optimizing lists with ListItem, or reviewing RNE component code.

Application-level React performance optimization covering React Compiler mastery, bundle optimization, rendering performance, data fetching, Core Web Vitals, state subscriptions, profiling, and memory management. Use when optimizing React app performance, analyzing bundle size, improving Core Web Vitals, or profiling render bottlenecks. Complements the react skill (API-level patterns) with holistic performance strategies. Does NOT cover React 19 API usage (see react skill) or Next.js-specific features (see nextjs-16-app-router skill).

Architectural refactoring guide for React applications covering component architecture, state architecture, hook patterns, component decomposition, coupling and cohesion, data flow, and refactoring safety. Use when refactoring React codebases, reviewing PRs for architectural issues, decomposing oversized components, or improving module boundaries. Does NOT cover React 19 API usage (see react skill) or performance optimization (see react-optimise skill).

React Testing Library best practices for writing maintainable, user-centric tests. Use when writing, reviewing, or refactoring RTL tests. Triggers on test files, testing patterns, getBy/queryBy queries, userEvent, waitFor, and component testing.

Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles (formerly refactoring). This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.

Opinionated SwiftUI architecture enforcement for iOS 26 / Swift 6.2 clinic modular MVVM-C apps using local SPM package boundaries. Enforces App-target `DependencyContainer` + route shells, @Observable ViewModels/coordinators, Domain repository/coordinator/error-routing protocols, Data-owned I/O, stale-while-revalidate reads, and optimistic queued sync. Use when writing, reviewing, or refactoring SwiftUI architecture, navigation, dependency wiring, or repository boundaries.

Tailwind CSS v4 performance optimization and best practices guidelines (formerly tailwindcss-v4-style). This skill should be used when writing, reviewing, or refactoring Tailwind CSS v4 code to ensure optimal build performance, minimal CSS output, and correct usage of v4 features. Triggers on tasks involving Tailwind configuration, @theme directive, utility classes, responsive design, dark mode, container queries, or CSS generation optimization.

Corrects the wrong defaults a model has when designing a relational schema — the DDL decisions an experienced engineer makes differently. Use when creating or reviewing tables, migrations, ER models, or ORM schema definitions. Covers identity and keys (surrogate vs natural, identity vs serial, uuidv7, composite keys that make cross-tenant references impossible), relationships (polymorphic foreign keys the database cannot enforce, referential actions, unindexed FK columns, disjoint subtypes), invariants the engine can prove instead of application code (EXCLUDE, partial unique indexes, CHECK limits, deferrable cycles, NOT VALID), types (timestamptz, exact money, range types, enum vs lookup table), derived and encoded data (generated columns, JSONB as an escape hatch), and time (events vs in-place updates, soft-delete flags that silently disable constraints, temporal keys). NOT for query tuning, index selection for read paths, or connection pooling.

Distills a logical relational-database design methodology into rules an agent applies while designing or reviewing a schema. Covers the design sequence (mission → tables → fields → keys → relationships → business rules → views → integrity review), one-subject-per-table decomposition, atomic single-valued fields, candidate/primary/foreign keys, relationship types with deletion rules and participation, the four levels of data integrity, database-vs-application business rules, validation tables, views for derived data, and the flat-file / spreadsheet / RDBMS-driven antipatterns to avoid. The structure is logical and RDBMS-agnostic, and normalized by construction. Use when designing a new relational schema, reviewing or refactoring an existing one, resolving redundant or repeating data, choosing keys, modeling relationships, or deciding where a constraint belongs.

RESTful API design guidelines following the Richardson Maturity Model through to Level 3 (HATEOAS) for Ruby on Rails. This skill should be used when designing, building, reviewing, or refactoring REST APIs to ensure proper resource modeling, HTTP method semantics, hypermedia controls, content negotiation, and API evolvability. Triggers on tasks involving API controllers, serializers, routing, link relations, pagination, error handling, or HTTP caching in Rails.

Ruby performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Ruby code to ensure optimal performance patterns. Triggers on tasks involving object allocation, collection processing, ActiveRecord queries, string handling, concurrency, or Ruby runtime configuration.

Ruby refactoring guidelines from community best practices. This skill should be used when refactoring, reviewing, or restructuring Ruby code to improve design, readability, and maintainability. Triggers on tasks involving code smells, method extraction, conditional simplification, coupling reduction, design patterns, or Ruby idiom adoption.

Corrects the wrong defaults a model has when standing up self-hosted SonarQube Server as a continuous gate on AI-generated code, verified against docs.sonarsource.com in July 2026 (Server 2026.3, LTA 2026.1). Use when configuring SonarQube, designing a quality gate, or wiring a scan step into CI for AI-assisted work. Covers the setups that run green while measuring almost nothing — the fudge factor skips duplication and coverage conditions below 20 new lines and is on by default; duplication is never measured on test code; no `new_cognitive_complexity` metric exists to gate on; `sonar.host.url` now defaults to sonarcloud.io rather than localhost; a shallow clone degrades new-code attribution to timestamps. Also covers AI Code Assurance — the project flag, gate qualification, the deprecated Copilot autodetection — and that Community Build cannot analyze pull requests at all. NOT for SonarQube Cloud, IDE connected mode, or authoring custom analyzer rules.

Use this skill when writing, reviewing, or refactoring Elixir, OTP, Ecto, or Phoenix/LiveView code. It corrects the staff-level judgment calls a capable model gets wrong by default — reaching for a GenServer as the universal tool, rescuing instead of letting processes crash, N+1 Ecto access, non-atomic writes, unbounded/linked Task fan-out, atom exhaustion from user input, eager Enum where Stream fits, and calling Repo from the web layer. Applies whenever the work touches process design, error handling, concurrency, data access, or LiveView, even if the user doesn't name them.

Use whenever creating, configuring, or extending Storybook for a TS/React component library — covers main.ts/preview.ts setup, CSF3 story authoring, args/argTypes/controls, decorators and providers, MSW and module mocking, play-function interaction tests via the Vitest addon, the @storybook/addon-a11y workflow (axe-core), autodocs vs MDX docs, design tokens, Figma linking, Chromatic deployment, and on-demand build performance. Triggers on tasks like "write a story", "set up Storybook", "configure addon-a11y", "fix this play function", "deploy Storybook", "test Storybook in CI" — even when the user doesn't say "storybook" but is editing `*.stories.tsx`, `.storybook/main.ts`, or `.storybook/preview.ts`. Targets Storybook 9+/10 (modern `storybook/test` import, Vitest addon, CSF3 + `satisfies Meta`). Does NOT cover generic React patterns (use the `react` skill), generic Testing Library queries (use `react-testing-library`), or WCAG primer (points at addon-a11y + axe rule config).

JSON HTTP API design rules distilled from Stripe — resource modeling, identifier schemes, URL structure, request/response wire format, pagination, errors, idempotency, versioning, naming, webhooks, and authentication. Triggers on tasks involving OpenAPI specs, API design reviews, schema decisions, endpoint shaping, error envelope design, webhook delivery, or any "is this API well-designed" question. Apply when designing, reviewing, or refactoring a JSON HTTP API — even when the user doesn't mention Stripe by name, since the rules are general API-design principles distilled from the industry's most-copied reference.

SwiftData persistence and data-layer architecture for iOS 26 / Swift 6.2 clinic modular MVVM-C apps. Use when writing, reviewing, or refactoring @Model entities, repository implementations, stale-while-revalidate reads, optimistic queued writes, sync/retry behavior, and SwiftUI integration that keeps SwiftData types inside Data-only boundaries.

Swift 6.2 and SwiftUI performance optimization for iOS 26 clinic architecture codebases. Covers async/await concurrency patterns, Sendable/actor isolation, view/render performance, and animation performance while preserving modular MVVM-C boundaries across App, Feature, Domain, and Data layers. Use when profiling or optimizing Swift/SwiftUI behavior in clinic modules.

Swift and SwiftUI refactoring patterns aligned with the iOS 26 / Swift 6.2 clinic modular MVVM-C architecture (Airbnb + OLX SPM layout). Enforces @Observable ViewModels/coordinators, App-target `DependencyContainer` + route shells, Domain repository/coordinator/error-routing protocols, and Data-owned I/O with stale-while-revalidate plus optimistic queued sync boundaries. Use when refactoring existing SwiftUI code into the clinic architecture.

Responsive UI transformation patterns for Tailwind CSS applications. This skill should be used when making interfaces responsive, refactoring layouts for multiple screen sizes, or reviewing responsive Tailwind code. Triggers on tasks involving breakpoint strategy, layout adaptation, responsive spacing, fluid typography, mobile navigation, touch interaction, responsive media, or data table responsiveness.

Refactoring UI design patterns for Tailwind CSS applications. This skill should be used when writing, reviewing, or refactoring HTML with Tailwind utility classes to improve visual hierarchy, spacing, typography, color, depth, and polish. Triggers on tasks involving UI cleanup, design review, Tailwind refactoring, component styling, or visual improvements.

TanStack Query v5 performance optimization for data fetching, caching, mutations, and query patterns. This skill should be used when using useQuery, useMutation, queryClient, prefetch patterns, or TanStack Query caching. This skill does NOT cover generating query hooks from OpenAPI (use orval skill) or mocking API responses in tests (use test-msw skill).

Test-Driven Development methodology and red-green-refactor workflow (formerly test-tdd). This skill should be used when practicing TDD, writing tests first, designing tests before implementation, or reviewing test-first approaches. Triggers on "write tests first", "test before code", "red green refactor", "test driven development". This skill does NOT cover Vitest framework specifics (use vitest skill) or API mocking with MSW (use msw skill).

Terminal User Interface (TUI) performance and UX guidelines for TypeScript applications using Ink and Clack. This skill should be used when building CLI tools, interactive terminal prompts, or developer tooling with TUI components. Triggers on tasks involving TUI components, CLI prompts, terminal rendering, keyboard input handling, or developer tooling.

Corrects the wrong defaults a model has for tRPC v11 (verified against 11.18.0). Use when code imports initTRPC, createTRPCContext, createTRPCOptionsProxy, createTRPCReact, httpBatchLink, httpSubscriptionLink, fetchRequestHandler, or TRPCError, or when building routers, procedures, middleware, links, or RSC prefetching. Covers the v10 to v11 drift that makes stale code fail — the React client that flipped to @trpc/tanstack-react-query, transformers moving into links, renamed type exports, observable subscriptions replaced by async generators over SSE — plus the defaults that are unsafe rather than merely stale — uncapped batching, CDN cache headers that serve one user's data to another, stack traces shipped from edge runtimes, and middleware that runs before input validation. NOT for TanStack Query semantics generally (use tanstack-query), Zod schema authoring (use zod), or Next.js App Router routing (use nextjs).

Google TypeScript style guide for writing clean, consistent, type-safe code. This skill should be used when writing, reviewing, or refactoring TypeScript code. Triggers on TypeScript files, type annotations, module imports, class design, and code style decisions.

TypeScript performance, tsconfig, type errors, async patterns — triggered when the user asks to "optimize TypeScript performance", "speed up tsc compilation", "configure tsconfig.json", "fix type errors", "improve async patterns", or encounters TS errors (TS2322, TS2339, "is not assignable to"). Also triggers on .ts, .tsx, .d.ts file work involving type definitions, module organization, or memory management. Does NOT cover TypeScript basics, framework-specific patterns, or testing.

Strict design and UX rules for React 19 + Next.js 16 (App Router) + Tailwind CSS 4. Covers navigation, interaction design, accessibility, user feedback, UX patterns, and visual design. Use when designing, building, or reviewing any user-facing web feature on this stack. Trigger when the user asks to "build a settings page", "add a dialog", "design this form", "review for accessibility", "fix dark mode", or any Next.js App Router / React 19 / Tailwind UI task. Also trigger when the user says output "looks off", "isn't accessible", or "doesn't follow best practices."

Apply the Theory of Constraints (Goldratt's Five Focusing Steps) to find and fix the single bottleneck that caps a process, workflow, pipeline, or Agent Skill/plugin's throughput. Use whenever optimizing speed, lead time, cost, or token/context budget of a system — CI/build pipelines, dev value streams (idea→review→merge→ship), an Agent Skill's trigger/context flow, or runtime code paths — ESPECIALLY when you don't know where to optimize, a local speedup didn't improve the whole, work piles up at a stage, everything is busy but little ships, adding capacity didn't help, or a policy/rule (not a resource) is the limiter. It locates the constraint with measurement, then prescribes exploit → subordinate → elevate → repeat, and stops you optimizing non-constraints (the "mirage of the non-bottleneck"). Trigger even when the user just says "speed this up", "why is this slow", or "make this more efficient" without mentioning constraints or bottlenecks.

Deep strategic thinking mode that finds the single highest-leverage, most innovative action by blending concepts across domains. Use this skill whenever the user asks you to think, brainstorm, strategize, or figure out what to do next — even casually. Trigger on phrases like "what should we do", "what's the best approach", "what would you suggest", "think about this", "what's the smartest move", "I'm stuck", "ideas?", "hmm what if we...", "what's next", "how should we approach", or any request for creative/strategic ideation rather than straightforward execution. When in doubt about whether the user wants execution or ideation, lean toward triggering this skill.

Security threat modeling, attack surface mapping, and trust boundary analysis on a codebase. Triggers on 'threat model', 'security review', 'attack surface', 'trust boundaries', or when assessing a project's security posture. Also trigger when the user is about to build security-sensitive features (auth, crypto, file I/O, network services, native bridges) and needs to understand the threat landscape first — even if they don't explicitly say "threat model." Also triggers on 'what changed' or 'diff analysis' for incremental security review of recent commits.

Remediate security findings by producing minimal, surgical code patches. Triggers on 'patch security findings', 'fix vulnerabilities', 'remediate findings', 'threat patch', or when the user provides a findings.json (from threat-model), a Codex security findings CSV, a THREAT-MODEL.md, or individual vulnerability descriptions and wants them fixed. Also trigger when reviewing code flagged by a security scanner and the user wants actionable fixes rather than just reports.

Advanced TypeScript — type-level programming, library/DSL APIs, declaration merging, modern language features at depth (decorators, using, const T, NoInfer, variance), and feature implementation patterns built on advanced types. Trigger on tasks involving recursive conditional types, infer patterns, mapped-type key remapping, variadic tuples, fluent builders with phantom state, schema-first inference (Zod/Valibot), end-to-end-typed API clients, finite state machines, module augmentation, and library-publishing concerns. Trigger even when the user does not say "advanced" — if the work involves type-level algorithms, library-author API design, or going beyond surface-level uses of TS 5.x features, this is the skill. Assumes the reader has absorbed the `typescript-refactor` skill — this one extends those patterns at depth, never restates them.

TypeScript and TSX refactoring and modernization guidelines from a principal specialist perspective, current to TypeScript 6.0 and React 19. This skill should be used when refactoring, reviewing, or modernizing TypeScript or React/TSX code for type safety, compiler performance, and idiomatic patterns. Triggers on tasks involving type architecture, narrowing, generics, discriminated unions, error handling, React component and hook typing, or migration to modern TypeScript features (satisfies, using, const type parameters, inferred type predicates, isolatedDeclarations, erasable syntax, import attributes).

UI/UX and frontend design best practices guidelines (formerly frontend-design). This skill should be used when writing, reviewing, or designing frontend code to ensure accessibility, performance, and usability. Triggers on tasks involving HTML structure, CSS styling, responsive layouts, form design, animations, or accessibility improvements.

Uniwind best practices for React Native styling with Tailwind CSS. This skill should be used when writing, reviewing, or refactoring React Native code using Uniwind. Triggers on tasks involving Uniwind, className styling, Tailwind in React Native, NativeWind migration, or theming.

UNIX command-line interface guidelines for building tools that follow POSIX conventions, proper exit codes, stream handling, and the UNIX philosophy. This skill should be used when writing, reviewing, or designing CLI tools to ensure they integrate properly with the UNIX tool chain. Triggers on tasks involving CLI tools, command-line arguments, exit codes, stdout/stderr, signals, or shell scripts.

VHS terminal recording best practices from Charmbracelet (formerly charmbracelet-vhs). This skill should be used when writing, reviewing, or editing VHS tape files to create professional terminal GIFs and videos. Triggers on tasks involving .tape files, VHS configuration, terminal recording, demo creation, or CLI documentation.

Vite performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Vite configuration and projects to ensure optimal performance patterns. Triggers on tasks involving Vite config, build optimization, dependency pre-bundling, plugin development, bundle analysis, or HMR issues.

Vitest testing framework patterns for test setup, async testing, mocking with vi.*, snapshots, and test performance (formerly test-vitest). This skill should be used when writing or debugging Vitest tests. This skill does NOT cover TDD methodology (use test-tdd skill), API mocking with MSW (use test-msw skill), or Jest-specific APIs.

Designs React 19 + Next.js 16 + Tailwind CSS experiences with real taste — starting from user goals, not pixels. Use this skill whenever the user asks you to build a page, screen, form, dashboard, or any user-facing web UI. Trigger when the user says "build a settings page", "create a dashboard", "design this properly", "I want this to feel premium", "make it look like Linear/Stripe/Vercel", or any Next.js App Router / React / Tailwind UI task. Also trigger when reviewing UI for design quality, or when the user says the output "looks like a wireframe", "feels like Bootstrap", or "looks AI-generated." When building any user-facing web view, lean toward triggering this skill.

Writing webpack 5 plugins — hook selection (compiler vs compilation, tap vs tapAsync, processAssets stages), the asset pipeline (emitAsset, source classes, info metadata, source maps), watch-mode and persistent caching (file/context/missing/buildDependencies), plugin lifecycle (constructor purity, multi-compiler isolation, shutdown cleanup), schema-utils validation, WebpackError reporting, jest-worker parallelism, and compatibility patterns (compiler.webpack namespace, peerDependencies, getCompilationHooks WeakMap). Patterns are drawn from production plugins like mini-css-extract-plugin, terser-webpack-plugin, compression-webpack-plugin, and Next.js's webpack plugins. Trigger when writing, reviewing, or debugging webpack 5 plugins — even if the user doesn't explicitly mention "best practices" — anytime an `apply(compiler)` method is being written, hooks are being tapped, or a plugin imports from `webpack-sources`, the rules in this skill apply.

Use whenever you face a webpack-build problem that ends with needing to write a plugin — 26 production-shaped recipes covering bundle-size budgets, forbidden architectural imports, env-var validation, secret-leak prevention, build-info injection, asset manifests, license walking, SRI hashes, virtual modules (Vite-style), filesystem routing (Next.js-style), generated barrels, runtime-driven TS types, library replacement (react→preact), debug-stripping, conditional polyfills, dynamic banners, feature flags, build-duration regression tracking, desktop/Slack notifications, changed-chunks diffing, browser auto-open, gzip/brotli pre-compression, image optimization, type-based dist layout, empty-chunk cleanup, cache-busting query strings. Each recipe is a complete working plugin drawn from production patterns at Next.js, Storybook, webpack-contrib. Trigger when the user asks how to do X with webpack or whether a plugin exists for X. Companion to webpack-plugin-authoring.

Write, revise, or review software design documents using Malte Ubl's "Design Docs at Google" guidance. Use when Codex needs to draft a design doc, technical design, RFC, engineering review doc, architecture proposal, mini design doc, or design-doc review focused on context, goals, non-goals, trade-offs, alternatives, cross-cutting concerns, review lifecycle, and deciding whether a design doc is warranted.

WXT browser extension performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring WXT browser extension code to ensure optimal performance patterns. Triggers on tasks involving WXT, browser extensions, content scripts, service workers, messaging, and extension APIs.

Corrects the wrong defaults a capable model has when building Yjs collaborative editing into a Next.js 16 App Router app with tRPC v11 and shadcn/ui — pinned to Yjs 13.6.31. Covers the decisions whose failure mode is silence rather than an error — plain objects in a Y.Map discarding concurrent edits, updates that vanish because a JSON boundary turned a Uint8Array into a number array, undo that reverts a colleague's paragraph, initial content seeded once per client, and carets thrown to the end of a shadcn Input on every remote keystroke. Also covers where the sync loop can actually run, since route handlers cannot upgrade, Vercel Functions have no instance affinity, and tRPC subscriptions are one-way — plus which packages have already moved to the Yjs 14 prerelease track. Use when writing, reviewing, or debugging collaborative editing, presence, offline sync, or CRDT persistence in this stack.

Zod schema validation best practices for type safety, parsing, and error handling. This skill should be used when defining z.object schemas, using z.string validations, safeParse, or z.infer. This skill does NOT cover React Hook Form integration patterns (use react-hook-form skill) or OpenAPI client generation (use orval skill).

Zustand state management best practices for React applications. Use when writing, reviewing, or refactoring Zustand stores to ensure optimal performance and maintainability. Triggers on tasks involving state management, stores, selectors, re-renders, and Zustand patterns.