Documents edge cases, error states, boundary conditions, and recovery paths for features. Use during specification for coverage or QA planning for test scenarios.
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references/EXAMPLE.mdreferences/TEMPLATE.mdDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
An edge cases document systematically catalogs the unusual, boundary, and error scenarios for a feature. While happy-path flows are typically well-specified, edge cases often get discovered in production — causing bugs, poor user experience, and support burden. Documenting edge cases upfront ensures engineering handles them intentionally and QA knows what to test.
When asked to document edge cases, follow these steps:
Define the Feature Scope Clearly describe what feature or flow you're analyzing. Edge cases are specific to context — the same input might be valid in one feature and invalid in another.
Walk Through Input Validation Consider every user input: What if it's empty? Too long? Wrong format? Contains special characters? What are the minimum and maximum valid values?
Explore Boundary Conditions Find the edges of acceptable ranges. If a field accepts 1-100, test 0, 1, 100, and 101. Consider pagination boundaries, timeout thresholds, and rate limits.
Map Error States Identify what can go wrong: network failures, permission denied, resource not found, concurrent modifications, expired sessions. Document both the scenario and expected behavior.
Consider Concurrency Issues What if two users act simultaneously? What if the user double-clicks? What if data changes between load and save? Race conditions often cause subtle bugs.
Define Recovery Paths For each error, specify how users recover. What message do they see? Can they retry? Is data preserved? Good error handling turns frustration into confidence.
Prioritize by Likelihood and Impact Not all edge cases need the same attention. High-likelihood + high-impact cases need robust handling; rare + low-impact cases might just need graceful failure.
Use the template in references/TEMPLATE.md to structure the output.
Before finalizing, verify:
See references/EXAMPLE.md for a completed example.