Lobster Debugging
by 王晓菲
A systematic 4-phase debugging framework to find root causes, eliminate flaky tests, and prevent regressions.
Free
Works with the AI tools you already use
See it in action
You say
The auth provider is flaky on slow connections; I tried adding a sleep but it still fails sometimes. Use the Lobster framework to find a permanent fix.
Your agent does
[ROOT CAUSE FOUND]: Race condition in Auth Provider. [PREVENTION]: Replaced 500ms sleep with Promise.all() synchronization. [UNIT TEST]: test_auth_persistence_on_slow_network verified. [REGRESSION]: Validated against 4 existing auth edge cases. Result: Bug eliminated, not patched.
About this skill
What it does
Lobster Debugging is a rigorous, 4-phase framework designed to eliminate the "guess-and-check" cycle that plagues modern software development. It transforms your AI agent from a simple code-patcher into a systematic forensic investigator. Instead of applying shallow fixes, it enforces a strict protocol: investigation, condition-based synchronization, defense-in-depth, and academic verification.
Why use this skill
Standard LLMs often rush to provide the first plausible-looking fix, which leads to regression and technical debt. This skill implements the 'Iron Law' of debugging: no fixes allowed without proven root cause isolation. It prevents common pitfalls like "magic" sleep timers, flaky test patches, and symptom-only repairs. By using this skill, you ensure that every bug fixed is a bug that never returns.
How it works
- Phase 1: Investigation: Systematic binary search of the codebase and diagnostic instrumentation.
- Phase 2: Synchronization: Replacing flaky timeouts with robust event-based waiting.
- Phase 3: Defense-in-Depth: Implementing guards that prevent entire classes of similar vulnerabilities.
- Phase 4: Verification: Proving the fix handles edge cases and handles regression testing.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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