fetch-url
获取并提取链接正文（默认 Markdown）；内置 X/Twitter URL 处理，提升受限页面的抓取成功率。
git-commit
处理 git 提交/推送/分支命名与提交信息规范；当用户要求 commit、push、起分支或整理 commit message 时使用。
github-cli
使用 GitHub CLI 与 GitHub 资源交互；适用于 repo、issue、PR、comment、release、workflow 等查看、更新或创建场景。
gitlab-cli
使用 GitLab CLI（glab）与 GitLab 资源交互；适用于 project、issue、MR、comment、wiki 等查看、更新或创建场景，含自建实例。
ticktick-cli
使用 Python CLI 与 Dida365 Open API 交互以管理滴答清单任务/项目，适用于需要通过脚本或命令行调用滴答清单接口的场景（如项目/任务的查询、创建、更新、完成、删除）。
pwdebug
用于需要通过命令行操作真实浏览器实例进行前端调试（如导航、执行 JS、截图、元素拾取、控制台日志）且希望跨多次命令复用同一浏览器会话的场景。
resend-design-skills
Use when needing Resend design resources. Routes to brand guidelines, visual identity, UI components, and design tokens.
resend-brand
Use when creating Resend marketing materials, documents, presentations, or visual content. Triggers for Resend brand, Resend style, or Resend visual identity requests.
resend-design-system
Use when building or modifying UI in the Resend codebase. Provides component APIs, variant options, design tokens, and composition patterns for all src/ui/ primitives.
trigger-cost-savings
Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Requires Trigger.dev MCP tools for run analysis.
trigger-agents
AI agent patterns with Trigger.dev - orchestration, parallelization, routing, evaluator-optimizer, and human-in-the-loop. Use when building LLM-powered tasks that need parallel workers, approval gates, tool calling, or multi-step agent workflows.
trigger-setup
Set up Trigger.dev in your project. Use when adding Trigger.dev for the first time, creating trigger.config.ts, or initializing the trigger directory.
trigger-tasks
Build AI agents, workflows and durable background tasks with Trigger.dev. Use when creating tasks, triggering jobs, handling retries, scheduling cron jobs, or implementing queues and concurrency control.
trigger-realtime
Subscribe to Trigger.dev task runs in real-time from frontend and backend. Use when building progress indicators, live dashboards, streaming AI/LLM responses, or React components that display task status.
trigger-config
Configure Trigger.dev projects with trigger.config.ts. Use when setting up build extensions for Prisma, Playwright, FFmpeg, Python, or customizing deployment settings.
PRD Mastery: Context-Aware, Expert-Driven, and Token-Efficient Refinement
A skill that blends the wisdom of top industry experts, ensures token-efficient PRDs, and organizes outputs in a clear folder structure.
authjs-skills
Auth.js v5 setup for Next.js authentication including Google OAuth, credentials provider, environment configuration, and core API integration
ai-sdk-6-skills
AI SDK 6 Beta overview, agents, tool approval, Groq (Llama), and Vercel AI Gateway. Key breaking changes from v5 and new patterns.
clerk-nextjs-skills
Clerk authentication for Next.js 16 (App Router only) with proxy.ts setup, migration from middleware.ts, environment configuration, and MCP server integration.
mcp-server-skills
Pattern for building MCP servers in Next.js with mcp-handler, shared Zod schemas, and reusable server actions.
prisma-orm-v7-skills
Key facts and breaking changes for upgrading to Prisma ORM 7. Consider version 7 changes before generation or troubleshooting
resend-integration-skills
Integrate Resend email service via MCP protocol for AI agents to send emails with Claude Desktop, GitHub Copilot, and Cursor. Set up transactional and marketing emails, configure sender verification, and use AI to automate email workflows.
upstash-vector-db-skills
Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.
vespertide
Define database schemas in JSON and generate migration plans. Use this skill when creating or modifying database models, defining tables with columns, constraints, and ENUM types for Vespertide-based projects.
langsmith-fetch
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
coordination
Multi-agent coordination for parallel plan execution with the coordinate and coord_output tools.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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>
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.
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.
gh-cli
This skill should be used when working with GitHub CLI (gh) for any task including code search, workflow debugging, GitHub Pages deployment, or general GitHub operations. Use this skill for enhanced code search capabilities, analyzing failed workflow runs, managing GitHub Pages, or any gh command usage.
github-actions-writer
This skill should be used when users need to create, modify, optimize, or troubleshoot GitHub Actions CI/CD workflows. Use when users ask about automating builds, tests, deployments, or any GitHub Actions-related tasks. Triggers include requests like "create a CI workflow", "deploy to AWS", "automate testing", "setup GitHub Actions", or when debugging workflow failures.
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