Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
breakup-pr
by lightdashBreak up a large PR into vertical feature slices delivered incrementally via Graphite stacked PRs. All verticals share a single feature flag so the entire feature ships atomically. Use when: splitting a large PR, breaking up a diff, vertical slicing, incremental delivery, phased rollout, or when a PR is too large to review.
fix-vulnerability
by lightdashFix a Snyk vulnerability PR by regenerating the pnpm lockfile, checking changelogs for breaking changes, and posting findings as a PR comment. Use when asked to fix a vulnerability PR or handle a Snyk dependency upgrade.
frontend-style-guide
by lightdashApply the Lightdash frontend style guide when working on React components, migrating Mantine v6 to v8, or styling frontend code. Use when editing TSX files, fixing styling issues, or when user mentions Mantine, styling, or CSS modules.
graphite
by lightdashManage stacked PRs with Graphite CLI (gt) instead of git for branch/PR operations. Auto-detects Graphite repos via .git/.graphite_repo_config. Use when: creating stacked PRs, navigating branches, submitting PRs, syncing with main, restacking after changes, or any gt command usage.
har-replay
by lightdashReplay a HAR file as a mock backend to reproduce frontend performance issues with production data. Use when asked to replay a HAR file, reproduce a dashboard with a HAR, or test frontend performance with captured traffic.
lightdash-agent-slack-messaging
by lightdashUse this skill when writing, designing, or generating Slack messages for Lightdash's in-app analytics agent. Triggers when someone asks to create agent update messages, Slack digests, agent notifications, weekly summaries, daily summaries, or any Slack copy for the Lightdash project agent. Also use when asked to vary, refresh, or make agent messages more engaging. Always use this skill for any Lightdash agent Slack communication, even if the user just says "write an agent message" or "draft a Slack update for the agent".
developing-in-lightdash
by lightdashUse when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, sql, set-warehouse), or creating/editing charts, dashboards, metrics, and dimensions as code
debug-local
by lightdashDebug the Lightdash app using PM2 logs, Spotlight traces, and browser automation. Use when investigating issues, tracking down bugs, understanding request flow, or correlating frontend actions with backend behavior.
ld-permissions
by lightdashGuide for Lightdash's CASL-based authorization system. Use when working with scopes, custom roles, abilities, permissions, ForbiddenError, authorization, or access control. Helps with adding new scopes, debugging permission issues, understanding the permission flow, and creating custom roles.
developing-in-lightdash
by lightdashUse when reading, creating, and editing Lightdash dashboards and charts as JSON, including dashboard layout and chart-type-specific configuration.
renovate-pr
by lightdashTest and assess an open Renovate dependency-bump PR. Picks the first open Renovate PR, checks out the branch, starts the app, exercises code paths affected by the upgraded package, reviews the changelog and (if needed) the upstream source diff, and reports whether the bump is safe to merge. Use when asked to "test a renovate PR", "triage renovate", "assess a renovate bump", or "check a dependency upgrade".
deprecate-endpoint
by lightdashUse when deprecating, sunsetting, or removing a backend HTTP API endpoint in Lightdash — wiring deprecation logging, deadline/sunset dates, response headers, and Sentry alerting onto a TSOA controller route. Also covers making the deprecation visible on docs.lightdash.com and llms.txt (description lead line, x-mint migration banner). Covers the first-party-caller precondition and the shared deprecation middleware.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.