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Found 170 Skills
Systematic debugging workflow — reproduce, investigate, hypothesize, fix, and prevent. Covers root cause analysis, bug category strategies, evidence-based diagnosis, and post-mortem documentation.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Fix a known bug in the Rock RMS codebase. Guides Claude through root cause analysis, minimal correct fix, and a release-note commit message. Use when the user says "fix this bug", "bugfix", "this is broken", "debug this", describes a bug with file paths or issue numbers, or pastes an error/stack trace with intent to fix. Also use when a bug is found by another skill (e.g. /review-conversion, /check) and the user wants it fixed. Do NOT use for: finding bugs (use /check or /review-conversion), adding features, or refactoring.
Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces. Detect whether host overhead is the bottleneck using GPU idle ratio, host prep exposed ratio, and per-phase evidence. For regressions, isolate forward steps via allreduce/NVTX patterns, compare host operation breakdowns across versions, and identify scheduling or request-management overhead. Supports optional inter-kernel gap, eager-vs-graph, pattern mapping, and multi-rank straggler drill-down. Use standalone or within perf-analysis. Triggers: host overhead, inter-step gap, scheduling overhead, forward step isolation, nsys iteration analysis, NVTX breakdown, request management overhead, GPU idle, host bottleneck, host prep exposed, inter-kernel gap, bubble analysis, graph coverage, eager kernel, rank imbalance, straggler detection.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Write a structured incident postmortem or post-incident review. Use when asked to write a postmortem, incident report, P1/P2 review, outage report, or RCA (root cause analysis). Generates a blameless postmortem with timeline, root cause, contributing factors, impact summary, and action items.
[Fix & Debug] ⚡⚡ Fix a GitHub issue with systematic debugging
Diagnose pytest or CI failures, identify root cause, and implement the minimal fix. Use when tests fail or CI reports errors.
Structured multi-step reasoning via Sequential Thinking MCP. Use for complex debugging, architectural trade-offs, or root cause analysis.
Collaboration workflow for GitHub Issue handling. Used when users receive an issue that needs analysis and response. Through the four-step process of "Diagnosis → Qualification → Decision → Response", produce accurate root cause analysis and appropriate user responses from an issue, avoiding misjudgment of problem types or unprofessional responses.
Debugging and Root Cause Localization for AscendC Operator Precision Issues. Used when operator precision tests fail (such as allclose failure, result deviation, all-zero/NaN output, etc.). Process: Error Distribution Analysis → Code Error-Prone Point Review → Experimental Isolation → printf/DumpTensor Instrumentation → Fix Verification. Keywords: precision debugging, precision issue, result inconsistency, error localization, allclose failure, output deviation, NaN, all-zero, precision debug.
Systematic Fishbone analysis exploring problem causes across six categories