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Found 15 Skills
Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process improvements.
Use this skill when working with CodeRabbit, such as running CodeRabbit reviews, generating and processing automated CodeRabbit comments, or evaluating CodeRabbit suggestions.
Review staged git changes against an issue. Produces a structured improvement plan — no edits applied. Also identifies test file compaction opportunities. Use when asked to check, review, or validate staged work before committing. Part 2 of 3 in the issue-review-two-phases workflow.
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.
Execute iterative refinement workflows with validation loops until quality criteria are met. Use for test-fix cycles, code quality improvement, performance optimization, or any task requiring repeated action-validate-improve cycles.
Comprehensive review of local uncommitted changes using specialized agents with code improvement suggestions
Enter this sub-process when conducting code optimization — handle tasks where 'behavior remains unchanged, structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step-by-step according to the method library, and require manual approval for each step'. Trigger scenarios: Users mention phrases like 'optimize it / refactor / rewrite / split it / poor performance / code is too long' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
Learn from PR outcomes. Analyzes accept/reject patterns and updates contribution lessons. Triggers: "pr retro", "learn from PR", "PR outcome", "why was PR rejected", "analyze PR feedback".
Systematic code refactoring following Martin Fowler's catalog. Methodologies: characterization tests, Red-Green-Refactor, incremental transformation. Capabilities: SOLID compliance, DRY cleanup, code smell detection, complexity reduction, legacy modernization, design patterns, functional programming patterns. Actions: refactor, extract, inline, rename, move, simplify code. Keywords: refactor, SOLID, DRY, code smell, complexity, extract method, inline, rename, move, clean code, technical debt, legacy code, design pattern, characterization test, Red-Green-Refactor, functional programming, higher-order function, immutability, pure function, composition, currying, side effects. Use when: improving code quality, reducing technical debt, applying SOLID principles, fixing DRY violations, removing code smells, modernizing legacy code, applying design patterns.
This skill should be used when the user asks to "remove AI slop", "clean up AI code", "remove AI patterns", "fix AI-generated code", "clean up PR", "remove unnecessary comments", "fix defensive checks", or mentions AI slop, AI code cleanup, or code quality issues from AI-assisted development. Identifies and removes unnecessary comments, defensive checks, type casts to any, and style inconsistencies.
Coordinates performance optimization: algorithm, query, and runtime workers in parallel
Self-directed iterative improvement system for Codex that cycles through modify, verify, retain/discard indefinitely