debug-memory-profiler
Profile memory usage to identify leaks and inefficiencies
Works with
--- name: debug-memory-profiler description: Profile memory usage to identify leaks and inefficiencies license: MIT --- # Debug Memory Profiler Profile memory usage to identify leaks and inefficiencies ## Risk Level **LOW** ## Core Rules - Profile accurately - identify leaks - Test thoroughly before deploying ## Response Pattern ### When Using This Skill 1. Profile memory 2. Validate the implementation 3. Test edge cases and error conditions 4. Ensure performance meets requirements ## Usage Contexts - Memory analysis - leak detection ## What NOT to Do - Inaccurate profiling - missed leaks - Deploy without testing ## Key Requirements - Understand the use cases before application - Follow the documented response pattern - Validate results in the target environment - Monitor for performance impact ## Further Learning Review related skills and documentation for deeper understanding of related systems and best practices.
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.
