pr-review
|
pr-review-orchestration
|
context-save
当用户发送"换窗口处理-"时调用。总结当前窗口的上下文信息、已完成任务、未完成任务，保存到 docs/context-sessions/ 目录，便于新窗口恢复。
context-resume
恢复之前保存的会话上下文。列出所有待处理的 session，读取选定 session 的任务信息，更新进度，任务全部完成后删除文件。
fabric-cli
Use Microsoft Fabric CLI (fab) to manage workspaces, semantic models, reports, notebooks, lakehouses, and other Fabric resources via file-system metaphor and commands. Use when deploying Fabric items, running jobs, querying data, managing OneLake files, or automating Fabric operations. Invoke this skill automatically whenever a user mentions the Fabric CLI, fab, or Fabric.
docker-compose-to-nixos
Converts Docker Compose configurations to NixOS modules using the dendritic pattern with Arion. Creates modules with system users, sops secrets, Arion docker-compose config, and Tailscale integration. Use when converting docker-compose.yaml files to NixOS modules or creating new Arion-based services.
nix-flake-init
Initializes Nix flake.nix files for projects using flake-parts, automatically detecting programming languages (OpenSCAD, Python) and configuring appropriate development shells. Activates when user asks to create, initialize, or add a flake.nix, set up Nix development environment, or add direnv support.
skill-making
Creates and refines Claude agent skills following best practices. Use when creating new skills, improving existing ones, or learning about skill structure and conventions.
btc-connect
专业的比特币钱包连接技能，支持btc-connect core、react、vue包在React、Vue、Next.js、Nuxt 3项目中的完整集成，包含UniSat和OKX钱包适配、网络切换功能、SSR环境配置、统一Hook API和v0.5.0最新特性
Pest Testing Framework
Integration with the Pest PHP testing framework for writing and running tests
fal-platform
fal.ai Platform APIs for model management, pricing, usage tracking, and cost estimation. Use when user asks "show pricing", "check usage", "estimate cost", "setup fal", "add API key", or platform management tasks.
fal-upscale
Upscale and enhance image resolution using AI. Use when the user requests "Upscale image", "Enhance resolution", "Make image bigger", "Increase quality", or similar upscaling tasks.
fal-image-edit
Edit images using AI on fal.ai. Style transfer, object removal, background changes, and more. Use when the user requests "Edit image", "Remove object", "Change background", "Apply style", or similar image editing tasks.
fal-audio
Text-to-speech and speech-to-text using fal.ai audio models. Use when the user requests "Convert text to speech", "Transcribe audio", "Generate voice", "Speech to text", "TTS", "STT", or similar audio tasks.
fal-generate
Generate images and videos using fal.ai AI models with queue support. Use when the user requests "Generate image", "Create video", "Make a picture of...", "Text to image", "Image to video", "Search models", or similar generation tasks.
fal-workflow
Generate production-ready fal.ai workflow JSON files. Use when user requests "create workflow", "chain models", "multi-step generation", "image to video pipeline", or complex AI generation pipelines.
skill-creator
>
rust-best-practices
>
rover
>
graphql-schema
>
apollo-server
>
apollo-mcp-server
>
apollo-connectors
>
graphql-operations
>
apollo-client
>
apollo-kotlin
>
swiftui-skills
Apple-authored SwiftUI and platform guidance extracted from Xcode. Helps AI agents write idiomatic, Apple-native SwiftUI with reduced hallucinations.
gitlab-skill
The model must apply when tasks involve .gitlab-ci.yml configuration, GitLab Flavored Markdown (GLFM) syntax, gitlab-ci-local testing, CI/CD pipeline optimization, GitLab CI Steps composition, Docker-in-Docker workflows, or GitLab documentation creation. Triggers include modifying pipelines, writing GitLab README/Wiki content, debugging CI jobs locally, implementing caching strategies, or configuring release workflows.
litellm
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
pre-commit
When setting up automated code quality checks on git commit. When project has .pre-commit-config.yaml. When implementing git hooks for formatting, linting, or validation. When creating prepare-commit-msg hooks to modify commit messages. When distributing a tool as a pre-commit hook.
mkdocs
Comprehensive guide for creating and managing MkDocs documentation projects with Material theme. Includes official CLI command reference with complete parameters and arguments, and mkdocs.yml configuration reference with all available settings and valid values. Use when working with MkDocs projects including site initialization, mkdocs.yml configuration, Material theme customization, plugin integration, or building static documentation sites from Markdown files.
fastmcp-creator
Build Model Context Protocol (MCP) servers - comprehensive coverage of generic MCP protocol AND FastMCP framework specialization. Use when creating any MCP server (Python FastMCP preferred, TypeScript/Node also covered). Includes agent-centric design principles, evaluation creation, Pydantic/Zod validation, async patterns, STDIO/HTTP/SSE transports, FastMCP Cloud deployment, .mcpb packaging, security patterns, and mid-2025+ community practices. Standalone skill with no external dependencies.
conventional-commits
When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.
commitlint
When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.
story-based-framing
This skill should be used when describing patterns or anti-patterns for detection by LLM agents across any domain (code analysis, business processes, security audits, UX design, data quality, medical diagnosis, etc.). Uses narrative storytelling structure ("The Promise" → "The Betrayal" → "The Consequences" → "The Source") to achieve 70% faster pattern identification compared to checklist or formal specification approaches. Triggers when creating pattern descriptions for any systematic analysis, detection tasks, or when delegating pattern-finding to sub-agents.
python3-development
The model must use this skill when : 1. working within any python project. 2. Python CLI applications with Typer and Rich are mentioned by the user. 2. tasked with Python script writing or editing. 3. building CI scripts or tools. 4. Creating portable Python scripts with stdlib only. 5. planning out a python package design. 6. running any python script or test. 7. writing tests (unit, integration, e2e, validation) for a python script, package, or application. Reviewing Python code against best practices or for code smells. 8. The python command fails to run or errors, or the python3 command shows errors. 9. pre-commit or linting errors occur in python files. 10. Writing or editing python code in a git repository.\n<hint>This skill provides : 1. the users preferred workflow patterns for test-driven development, feature addition, refactoring, debugging, and code review using modern Python 3.11+ patterns (including PEP 723 inline metadata, native generics, and type-safe async processing). 2. References to favored modules. 3. Working pyproject.toml configurations. 4. Linting and formatting configuration and troubleshooting. 5. Resource files that provide solutions to known errors and linting issues. 6. Project layouts the user prefers.</hint>
clang-format Configuration
The model must invoke this skill when any trigger occurs - (1) user mentions "clang-format" or ".clang-format", (2) user requests analyzing code style/formatting patterns/conventions, (3) user requests creating/modifying/generating formatting configuration, (4) user troubleshoots formatting behavior or unexpected results, (5) user asks about brace styles/indentation/spacing/alignment/line breaking/pointer alignment, (6) user wants to preserve existing style/minimize whitespace changes/reduce formatting diffs/codify dominant conventions.
hatchling
This skill provides comprehensive documentation for Hatchling, the modern Python build backend that implements PEP 517/518/621/660 standards. Use this skill when working with Hatchling configuration, build system setup, Python packaging, pyproject.toml configuration, project metadata, dependencies, entry points, build hooks, version management, wheel and sdist builds, package distribution, setuptools migration, and troubleshooting Hatchling build errors.
toml-python
When reading or writing pyproject.toml or .toml config files in Python. When editing TOML while preserving comments and formatting. When designing configuration file format for a Python tool. When code uses tomlkit or tomllib. When implementing atomic config file updates.
xdg-base-directory
When an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.
pypi-readme-creator
When creating a README for a Python package. When preparing a package for PyPI publication. When README renders incorrectly on PyPI. When choosing between README.md and README.rst. When running twine check and seeing rendering errors. When configuring readme field in pyproject.toml.
uv
Expert guidance for Astral's uv - an extremely fast Python package and project manager. Use when working with Python projects, managing dependencies, creating scripts with PEP 723 metadata, installing tools, managing Python versions, or configuring package indexes. Covers project initialization, dependency management, virtual environments, tool installation, workspace configuration, CI/CD integration, and migration from pip/poetry.
prompt-optimization-claude-45
Optimize CLAUDE.md files and Skills for Claude Code CLI. Use when reviewing, creating, or improving system prompts, CLAUDE.md configurations, or Skill files. Transforms negative instructions into positive patterns following Anthropic's official best practices.
verification-gate
Enforce mandatory pre-action verification checkpoints to prevent pattern-matching from overriding explicit reasoning. Use this skill when about to execute implementation actions (Bash, Write, Edit, MultiEdit) to verify hypothesis-action alignment. Blocks execution when hypothesis unverified or action targets different system than hypothesis identified. Critical for preventing cognitive dissonance where correct diagnosis leads to wrong implementation.
holistic-linting
This skill should be used when the model needs to ensure code quality through comprehensive linting and formatting. It provides automatic linting workflows for orchestrators (format → lint → resolve via concurrent agents) and sub-agents (lint touched files before task completion). Prevents claiming "production ready" code without verification. Includes linting rules knowledge base for ruff, mypy, and bandit, plus the linting-root-cause-resolver agent for systematic issue resolution.
agent-orchestration
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
brainstorming-skill
This skill should be used when users need to generate ideas, explore creative solutions, or systematically brainstorm approaches to problems. Use when users request help with ideation, content planning, product features, marketing campaigns, strategic planning, creative writing, or any task requiring structured idea generation. The skill provides 30+ research-validated prompt patterns across 14 categories with exact templates, success metrics, and domain-specific applications.
llamafile
When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.
async-python-patterns
Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
market-sentiment
Aggregate news from popular cryptocurrency RSS feeds, analyze sentiment of articles, and calculate an overall market sentiment score with detailed explanation. Use when assessing crypto market sentiment for trading decisions, research, or monitoring trends from RSS sources.
Page 815 of 1486 · 74266 results
