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.
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address-dependabot
by DataRecceUse when consolidating open Dependabot PRs into a single branch. Fetches all open Dependabot PRs from the repo, applies dependency updates locally, tests for breakage, and creates a single PR that closes all Dependabot PRs.
recce-mcp-e2e
by DataRecceUse when MCP server code is modified and needs full E2E verification against a real dbt project. Triggers after changes to recce/mcp_server.py, MCP tool handlers, single-env logic, or error classification. Also use before merging MCP PRs.
recce-mcp-dev
by DataRecceUse when modifying recce/mcp_server.py, MCP tool handlers, error classification, or MCP-related tests. Also use when adding new MCP tools or changing tool response formats.
linear-deep-dive
by DataRecceUse when given a Linear issue ID, URL, identifier, or project name/URL to analyze and plan work. For issues, fetches the issue, classifies it, explores relevant code, proposes an approach, and orchestrates the right skills. For projects, fetches the project with milestones and issues, builds a prioritized execution plan, and systematically works through issues respecting project structure and dependencies.
recce-verify
by DataRecceLightweight pre-commit verification for dbt model changes in the single-environment dev loop — when the user has a warehouse-connected dbt project but no `target-base/` artifacts. Triggers when: user asks to verify a model change, check whether an edit is safe to commit, sanity-check a filter/aggregation/join change without setting up a base environment, or asks for a quick risk read before running /recce-review. Uses Tier-1 evidence only — column lineage, AST analysis, and targeted current-env SQL probes. Routes to /recce-review when `target-base/` is fresh.
recce-guide
by DataRecceAutomatically provide Recce guidance in dbt projects. Triggers when: working in dbt project directory, discussing PRs or data changes, after dbt command execution, or when user asks about data validation.
python-uv-ci
by DataRecceGitHub Actions workflow steps for Python projects using uv. Use this skill when generating CI workflows that need uv-based dependency installation with virtual environment.
python-pip-ci
by DataRecceGitHub Actions workflow steps for Python projects using pip. Use this skill when generating CI workflows that need pip-based dependency installation with virtual environment.
recce-review
by DataRecceReview dbt model data changes using Recce. Triggers when: user asks to review data changes, check data impact, run recce review, validate model changes before committing, review a Recce Cloud PR session, connect MCP to a cloud session, pastes a GitHub PR / GitLab MR URL, or pastes a Recce Cloud session/launch URL for cloud-mode review.
recce-eval
by DataRecceUse when the user asks to "run eval", "recce eval", "evaluate plugin", "benchmark recce", "compare with plugin", "compare without plugin", "eval case", "score eval", "eval report", "eval history", "list eval scenarios", "list eval cases", "show eval history", "run eval case", or wants to measure the Recce Review Agent's effectiveness compared to pure Claude Code without the plugin.
readme-refresh
by DataRecceThis skill should be used when the user asks to "update readme", "review readme", "refresh readme", "audit readme", "fix the main readme", "改 readme", "檢查 readme", or when the root README.md needs to reflect new plugin changes, version bumps, or feature additions.
mcp-e2e-validate
by DataRecceUse when the user asks to "validate MCP", "run MCP E2E", "run E2E validation", "benchmark MCP performance", "test the plugin flow", "test MCP integration", "compare MCP versions", "show benchmark history", "驗證 MCP", "跑 E2E", "看歷史紀錄", or wants to verify the recce plugin's full event chain works end-to-end and measure agent performance metrics.
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.