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Found 81 Skills
Reliable end-to-end engineering workflow for debugging, root-cause analysis, minimal patching, and verification in production codebases. Use when Codex needs to investigate a failure systematically, trace execution, test hypotheses, implement a correct fix, validate the resolution, and check for regressions before declaring the task complete.
This skill should be used when the user asks to "fix a bug with minimal changes", "analyze change scope for a bugfix", "find the minimal set of files to change", "do a focused bugfix", or "scope a minimal repair".
Full debugging workflow — reproduce the bug with a failing test, perform root cause analysis, then implement a minimal fix.
· Audit AI-generated code slop: hallucinated APIs, over-abstraction, duplicate code, test theater, noisy comments. Triggers: 'slop', 'AI-generated code', 'cleanup', 'overengineered'. Not for prose (use anti-ai-prose).
Bug investigation and fix workflow. Triggers: 'debug', 'fix bug', 'investigate issue', 'something is broken', or /debug. Hotfix track for quick fixes, thorough track for root cause analysis. Do NOT use for feature development or refactoring. Do NOT escalate to /ideate unless the fix requires architectural redesign.
Align spec files with implementation. Detects drift between spec and code, surfaces discrepancies, user decides whether to update spec or code. Use when both a spec file and its implementation are in context.
Expertise in Senior Principal Engineering refactoring. Use when you need to eliminate technical debt, remove "AI Slop," simplify complex logic, and ensure DRY code.
Detect codebase bloat through progressive analysis: dead code, duplication, complexity, documentation bloat. Use when context usage high, quarterly maintenance, pre-release cleanup, before refactoring. Do not use when active feature development, time-sensitive bugs, codebase < 1000 lines.
Use when a repository needs cleanup of dead code, build artifacts, unused dependencies, outdated docs, or stale tests - provides safe cleanup workflows, validation steps, and reporting templates for code, deps, docs, tests, and sprint archives.
Simplify and clean code
Corrective cleanup of AI-generated code — removes LLM-specific patterns while preserving behavior. Use when the user says "clean up", "deslop", "slop", "clean AI code", or when you spot LLM-generated code smells after any generation session.
Keep code, tests, documentation, configuration, and other changes as tight, clear, performant, and minimal as possible without weakening behavior. Use during implementation to prevent unnecessary complexity, after behavior is proven to consolidate working changes, before handoff for a full branch audit, and when asked to simplify, tighten, clean up, reduce verbosity, remove complexity, or minimize existing work.