memory-leak
Memory leak detection and analysis for Node.js, Python, and browsers
Works with
--- name: memory-leak description: Memory leak detection and analysis for Node.js, Python, and browsers license: MIT --- # Memory Leak Detection & Analysis I'll help you detect, analyze, and fix memory leaks across your application stack. Arguments: `$ARGUMENTS` - runtime environment (node/python/browser) or specific files --- ## Token Optimization **Expected range**: 200–2,000 tokens (initial), 100 tokens (no leak pattern) **Caching**: Caches runtime detection in `.claude/cache/memory-leak/runtime.json` for 7 days. Invalidated when `package.json` changes. **Early exit**: Returns immediately if no memory leak patterns are detected. **Patterns used**: Grep-before-Read, early exit, Bash for system queries, caching
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.
