gpui-components
Generates Rust code for GPUI desktop UI components following Zed editor patterns. Use when building desktop applications with gpui crate, creating themed UI components, implementing autocomplete/completions, bu...
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Generates Rust code for GPUI desktop UI components following Zed editor patterns. Use when building desktop applications with gpui crate, creating themed UI components, implementing autocomplete/completions, bu...
解析 Codex CLI 输出日志,快速理解执行结果。触发词:/codex-parse、分析 codex 输出、查看 codex 日志
Process code review findings interactively - fix or skip issues from monty-code-review output. Presents issues in severity order, applies fixes, runs quality checks, and updates review documents with status mar...
Expert at automatically applying improvements to Claude Code components based on quality analysis. Enhances descriptions, optimizes tool permissions, strengthens security, and improves usability. Works in conju...
Analyze project stack and recommend skills. Auto-detects frameworks, activates generic ai-dev-kit skills, and optionally scaffolds project-specific skills in the target repo.
Complete VSCode Copilot asset management system with intelligent discovery, installation, and deployment capabilities for agents, prompts, instructions, collections, and skill packages.
Expert at analyzing the quality and effectiveness of Claude Code components (agents, skills, commands, hooks). Assumes component is already technically valid. Evaluates description clarity, tool permissions, au...
Self-improving skill system for Makepad development. Features self-evolution (accumulate knowledge), self-correction (fix errors automatically), self-validation (verify accuracy), usage feedback (track pattern...
Analyze files/directories for token optimization. Reports waste patterns and improvements. Use when: context limits, high costs, large codebases. Enforcement via .cursorrules.
Use this skill when working with ast-grep, an AST-based code search, lint, and rewrite tool. Activate when the user asks to search code patterns, refactor code structurally, create linting rules, or perform AST...
Automatically find relevant context from knowledge graph and code relationships while coding. Detects when context would be helpful (new files, unfamiliar code, architectural decisions) and surfaces related ent...
i-AUD Developer Kit 프로젝트 가이드. 프로젝트 구조, 폴더 설명, 개발 환경 설정, VS Code 확장 명령어에 대해 안내합니다. "i-AUD란?", "프로젝트 구조", "폴더 설명", "개발 환경", "AUD 명령어" 등을 물어볼 때 사용하세...
코드 품질 관리 스킬. Prettier/ESLint/TypeScript 컨벤션, Agent Skills 활용 패턴, 코드 리뷰 체크리스트.
Universal patterns for tree-sitter code parsing. Covers AST visitors, query patterns, and language plugin development. Framework-agnostic.
Extract coding standards and conventions from CONTRIBUTING.md, .editorconfig, linter configs. Use for onboarding and ensuring consistent contributions.
Search and answer questions about the ANcpLua ecosystem documentation. Use when users ask about ANcpLua.NET.Sdk features, ANcpLua.Analyzers rules, ANcpLua.Roslyn.Utilities APIs, or any configuration/usage quest...
Pointers for Copilot/agents to apply Perigon conventions
Automated lint and test smoke check workflow for code quality verification. Triggers when user requests code quality checks, lint execution, test validation, or smoke testing. Use for pre-commit checks or quali...
AI 코드 리뷰 결과 분석 및 개선 실행. pending 리뷰를 읽고, 개선 필요사항을 분석하여 실제 코드 수정까지 진행. 평가는 부산물.
Automated validation results analysis for post-commit workflow. Triggers when user requests validation analysis, code review summary, or checking background validation results. Analyzes Biome, TypeScript, and A...
This skill should be used when the user asks to "review code", "check for anti-patterns", "audit my code", "find issues in code", "review this file", "check this component", or mentions code review, linting, or...
Pack codebases into context-optimized formats for analysis and understanding.
Semantic code research across GitHub repositories for finding implementations, patterns, and conducting PR/security reviews.
Integrate or build a contenteditable-based editor with custom data models.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.