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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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>
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.
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