flaky-hunter
Diagnose and fix tests that pass sometimes and fail other times. Use when CI is red intermittently.
Works with
--- name: flaky-hunter description: Diagnose and fix tests that pass sometimes and fail other times. Use when CI is red intermittently. license: MIT --- # Flaky Test Hunter Run the suspect test 20x in a loop first — confirm it's actually flaky, not just broken. Common causes, in order of likelihood: 1. **Time/order** — depends on test execution order or shared mutable state. Isolate it; run alone. 2. **Async race** — asserting before a promise/refetch resolves. Await the actual condition, not a sleep. 3. **Real network/clock/random** — mock them. Freeze time, seed RNG, stub the call. 4. **Resource leak** — a prior test left a connection/file/port open. Fix the cause, not the symptom. `retry(3)` on a flaky test hides a real race that will bite in production. Quarantine only as a last resort, with a ticket.
More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
