skills

Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents from YAML, or test agent behavior.

Run offline Python (`pydantic_evals`) or Node.js (`logfire/evals`) evaluations and review them in Logfire. Also redirect existing Braintrust `Eval()` suites. Use for eval setup, test datasets, AI scoring, agent checks, LLM judges, Braintrust migration, or Logfire Datasets & Experiments. Not for live traffic or infrastructure monitoring.

Monitor hosts, Docker containers, Kubernetes clusters, database/queue/cache servers, and cloud-provider metrics with Pydantic Logfire — no application code required. Use this skill whenever the user asks to "monitor my host/server/VM", "monitor my Docker containers", "monitor my Kubernetes cluster", "send infrastructure metrics to Logfire", "watch my database/Postgres/Redis/MongoDB/Kafka", "collect cloud metrics" (AWS/GCP), or mentions the OpenTelemetry Collector in the context of Logfire. This is infrastructure only — for instrumenting APPLICATION CODE (traces, logs, AI/agent spans) use the logfire-instrumentation skill instead.

Add Pydantic Logfire observability to application code — traces, logs, metrics, and AI/agent spans. Use when the user asks to add or configure Logfire, observability, tracing, logging, or monitoring; maximize useful telemetry; or understand what an app is doing. Supports Python, JavaScript/TypeScript, Rust, and major AI agent frameworks including Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, LangChain, LangGraph, CrewAI, AutoGen, and Google ADK. For infrastructure-only monitoring (hosts, Docker, Kubernetes, databases, or cloud metrics with no app-code changes), use `logfire-infrastructure`. For evaluating AI/agent behavior against test datasets, use `logfire-evals`.

Query and analyze Logfire telemetry data — traces, logs, spans, metrics, summaries, and SQL results. Use this skill when the user asks to "query logfire", "search traces", "find logs", "query data", "search spans", "look up errors in logfire", "get metrics from logfire", "analyze telemetry", "summarize errors", "find root cause", or add Logfire querying capabilities to code. Do not use this skill for direct Logfire UI, browser, live-view, Explore-page, or link-opening requests; use logfire-ui instead. If "show" or "view" wording is ambiguous, ask whether the user wants a UI view or query analysis.

Entry point for Pydantic Logfire — an observability, monitoring, and evals platform. Use this skill when the user asks to "set up Logfire", "add Logfire to my project", "get me set up properly with Logfire", "send as much data as would be useful", mentions Logfire without a specific scope, or their request spans more than one of instrumenting application code / monitoring infrastructure / evaluating AI behavior. If the request is clearly scoped to exactly one of those, fetch that specific skill directly instead of this one — this skill exists to route, not to duplicate their content.

Open or return Logfire project pages, live views, trace links, and Explore pages in the Codex browser without querying telemetry first. Use this skill when the user asks to "open in Logfire", "show in the live view", "open Explore", "open the UI", "show in Codex", "use the browser", "give me a link", or asks for a Logfire GUI/browser/live-view presentation of a project, time range, service, span, trace, log, or filter. If "show" or "view" wording is ambiguous, ask whether the user wants a UI view or query analysis.

Migrate Python Agno applications to Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports `agno` or relies on Agno agents, teams, workflows, sessions, memory, knowledge, tools, hooks, guardrails, approvals, skills, streaming, or AgentOS.

Migrate Python applications from the Claude Agent SDK to Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports `claude_agent_sdk` or relies on Claude Code's agent loop, sessions, built-in tools, hooks, permissions, skills, or subagents. Do not use for the Anthropic Messages SDK or Claude Managed Agents.

Migrate Python Google Agent Development Kit (ADK) applications to Pydantic AI. Use for `LlmAgent`, `Runner`, sessions, state, memory, tools, callbacks, plugins, graph or dynamic workflows, resumability, events, artifacts, MCP, and ADK deployment boundaries.

Migrate Python LangChain, LangGraph, or Deep Agents applications to Pydantic AI and, when the source uses harness features, Pydantic AI Harness. Use for LangChain agents, chains, LCEL, direct LangGraph graphs, persistence, interrupts, streaming, and `create_deep_agent` projects with planning, filesystem or sandbox backends, skills, memory, subagents, permissions, approvals, or Deep Agents Code hosts.

Migrate TypeScript Mastra applications to Python with Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports `@mastra/*` or relies on Mastra agents, tools, workflows, memory, processors, streaming, approvals, skills, or subagents.

Migrate Python OpenAI Agents SDK applications to Pydantic AI and, when warranted, Pydantic AI Harness. Use for `agents.Agent`, `Runner`, function tools, handoffs, guardrails, sessions, human approval, streaming, or `SandboxAgent`. Do not use for applications built directly on the OpenAI Responses API without the Agents SDK runtime.

Migrate TypeScript Pi coding-agent applications, extensions, or packages to Python with Pydantic AI and Pydantic AI Harness. Use when source code imports `@earendil-works/pi-*`, calls `createAgentSession`, registers Pi extensions, or relies on Pi tools, hooks, skills, sessions, compaction, providers, TUI, RPC, or packages.

Migrate TypeScript Vercel AI SDK or Eve applications to Python with Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports `ai`, `@ai-sdk/*`, or `eve`, or relies on `generateText`, `streamText`, `ToolLoopAgent`, `useChat`, Eve agents, tools, skills, subagents, approvals, sessions, channels, schedules, or sandboxes.

Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- Code Mode (collapse many tool calls into one sandboxed Python execution), a filesystem and shell, sub-agents, planning, context compaction, and more. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want first-party filesystem/shell/sub-agent/planning/compaction capabilities for a Pydantic AI agent, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.