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...
tidb-verify-profile
by pingcapUse when choosing local validation scope in TiDB work, especially to separate fast coding-loop checks from completion checks and avoid unnecessary slow commands.
tidb-bazel-prepare-gate
by pingcapUse when deciding whether make bazel_prepare is required before build or test commands based on local file changes in TiDB.
tidb-pr-metadata-guard
by pingcapUse when creating or editing TiDB pull requests so PR title scope, PR template fields, hidden HTML comments, and bot-parsed checklist sections stay intact. Trigger on tasks involving PR creation, PR body updates, issue linking from a PR, test checklist updates, or investigating labels like do-not-merge/needs-tests-checked.
tidb-test-diff-triage
by pingcapTriage unexpected TiDB test diffs that seem unrelated to the current PR. Use when plan/result/testdata changes appear after merge/rebase or only in specific local runs, especially to quickly rule in/out failpoint enablement issues.
tidb-change-instruction-critic
by pingcapUse when implementing a user- or reviewer-prescribed code change (including review comments with suggested fixes or options), especially when the requested edit may be risky, incomplete, ambiguous, or misaligned with TiDB correctness and compatibility constraints.
tidb-failpoint-test-runner
by pingcapUse when running TiDB package tests and deciding whether failpoint enable/disable is required before and after the test command.
tidb-integrationtest-recorder
by pingcapUse when recording TiDB integration tests under tests/integrationtest and verifying regenerated result files stay minimal and correct.
tidb-issue-metadata-guard
by pingcapUse when creating or editing TiDB GitHub issues so issue templates, labels, issue titles, and issue descriptions stay consistent with repository workflow. Trigger on tasks involving issue creation, bug reports, enhancement tracking issues, label selection, or searching for existing issues and PRs before filing a new one.
tidb-realtikv-runner
by pingcapUse when running tests under tests/realtikvtest that require a local TiUP playground lifecycle with strict startup, readiness checks, and cleanup.
tidb-test-guidelines
by pingcapDecide where to place TiDB tests and how to write them (basic structure, naming, testdata usage). Use when asked about test locations, writing conventions, shard_count limits, casetest categorization, or when reviewing test changes in code review.
writing-doc-summaries
by pingcapWrites or updates the front matter `summary` field in pingcap/docs and pingcap/docs-cn Markdown files. The summary targets 115-145 characters, with a 45-character absolute minimum. Use when a document is missing a summary, when a reviewer or CI check flags a low-quality summary, or when an existing summary is outdated, inaccurate, or the wrong length.
release-notes
by pingcapEvaluate whether a change needs a release note. If yes, write, review, revise, or translate TiDB release note entries for the features, compatibility changes, improvements, and bug fixes sections. Use this skill when triaging PRs for release-note relevance, working with release note entries, aligning English and Chinese content, auditing `release-X.X.X.md` files, or editing files under `docs/releases/` or `docs-cn/releases/`.
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.