Edge Case Discovery

Discover boundary candidates across data domains, state models, time rules, resource limits, platform differences, and existing evidence. Produce EC-##. This is not a full requirement-quality review, full test-case authoring, threshold invention, or test execution.

When to Use

• Use it to systematically consider value, length, null/type, time, state, capacity, concurrency, platform, and combination boundaries.
• Use it to find high-risk boundaries outside the happy path from defects, failures, or design constraints.
• Use it to prioritize boundary candidates and turn them into verifiable follow-up test intent.

Do not use it only to analyze requirement gaps, write a complete test case suite, review existing cases, or execute boundary tests.

Output Format Options

• Use Markdown by default; when a table, CSV, or JSON is requested, preserve the same evidence, status, impact, owner, and validation fields.
• Do not present a structured format or static inventory as execution, pass, approval, or release evidence.

How to Use

1. Read this Skill's primary prompt and provide the objective, scope, material, environment, and available evidence.
2. Follow the prompt's input audit and output contract; deliver a bounded first pass when information is incomplete.
3. Retain source, evidence status, impact, owner role, close condition, and validation method for every finding.

Workflow

1. Read and follow prompts/edge-case-discovery.md, beginning with the six-part input audit.
2. Identify input, state/time/resource, and interaction dimensions and use only evidenced boundaries.
3. Record dimension, boundary/combination, trigger, concern, impact, evidence, and validation in EC-## entries.
4. Preserve assumptions and open questions for unknown thresholds, missing states, and conflicting rules.
5. Return discovery candidates rather than full cases; later test design and execution decide how to run them.

Core Constraints

• Consider value/length, null/type, time/timezone, state transitions, capacity/resources, concurrency/order, platform/localization, and combinations when applicable.
• Do not invent thresholds, states, concurrency counts, error results, or product rules; mark unknowns unassessed or open.
• EC-## is a candidate discovery, not executed, passed, complete-coverage, or zero-risk evidence.
• Do not expand candidates into full test cases, execute tests, or modify the target system.

Reference Files

• Always read prompts/edge-case-discovery.md before producing an analysis.
• For regression, read evals/eval.yaml and matching cases; configuration does not prove that boundaries were verified.
• For trigger checks, use evals/trigger-prompts.csv and evals/local-rules.json; missing selection trace is BLOCKED.

Best Practices

• Prioritize high-impact gaps with a verifiable next action, using the smallest useful experiment or evidence request.
• Separate facts, evidence-backed inferences, recommendations, and Human decisions; never upgrade an assumption into a conclusion.

Pre-delivery Check

• Recorded known facts, missing information, conflicts, stale information, out-of-scope items, and assumptions.
• Each EC-## has a dimension, boundary/combination, trigger, source, evidence state, impact, and validation suggestion.
• Known thresholds, inferred candidates, and unknown open items remain separate.
• Reasons and residual risks are stated for unassessed dimensions.
• The discovery list is not presented as full cases, execution results, pass evidence, or release conclusions.

Common Pitfalls

• Saying “test the boundary” without naming the dimension, trigger, and observable concern.
• Treating a common industry value as the current product threshold.
• Generating mechanical duplicate candidates for every field instead of prioritizing risk and evidence.
• Treating candidate count as proof of coverage quality.