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...
review-backport
by yugabyteReview YugabyteDB backport diffs by comparing them against their original commits/diffs. Use when the user provides a Phorge backport diff ID (e.g. D51405) and wants to verify the backport is correct, or when reviewing backport revisions.
create-diff
by yugabyteCreate a YugabyteDB Phorge diff for the current branch's changes. Use when the user wants to publish their changes for review.
create-pr
by yugabyteCreate a Pull Request for the current branch's changes. Use when the user wants to publish a branch for review as a GitHub PR.
update-yb-latest-stable
by yugabyteBump YugabyteDB latest_stable in yb-versions.json, coordinate Jenkins migtest cluster upgrades, fix version-gated issue/integration tests, and open a PR only after all GitHub Actions pass. Use when updating latest stable YB version, yb-versions.json, Jenkins YB cluster, or Voyager supported YB versions.
pr-description
by yugabyteCreate and update GitHub pull request descriptions using the project PR template. Use when the user asks to create a PR, write a PR description, update a PR description, or manage pull requests.
pr-review-sweep
by yugabyteSweep recently updated open GitHub PRs that don't yet have an AI review, and for each one run the branch-review skill and post the findings with the post-pr-review skill — fully automated, no per-PR confirmation. Use when the user asks to "sweep open PRs", "review all open PRs", "run the PR review sweep", or when invoked by the scheduled daily PR-review routine. Also accepts explicit PR numbers to force a review of specific PRs.
release-notes
by yugabyteGenerate yb-voyager release notes from a commit range, formatted like the YugabyteDB Voyager release-notes docs page. Use when the user asks to draft release notes, summarize a release, or write what's-new entries for a voyager version.
branch-review
by yugabyteReview all code changes on the current branch compared to main, applying hierarchical BUGBOT.md and .cursor/BUGBOT.md guidelines from each changed file's directory up to the repo root. Use when the user asks to review a branch, review changes, compare against main, do a code review, or check branch diff.
git-rebase
by yugabyteInteractive git rebase workflow with conflict-by-conflict review. Use when the user asks to rebase a branch, rebase onto main, resolve rebase conflicts, or update a branch with upstream changes.
post-pr-review
by yugabytePost a single non-blocking GitHub PR review with inline line-anchored comments generated by the agent. Every comment is prefixed with `[AI]` so reviewers can tell they are AI-generated. Only Critical and Warning findings are posted; Suggestions / Nice-to-haves / Good-to-haves are never sent. Use when the user asks to "post the review on GitHub", "post these comments on the PR", "post the findings as PR comments", or otherwise wants an in-chat code review persisted on a GitHub pull request. Typically runs as a follow-up to the `branch-review` skill but does not require it.
voyager-release-notes
by yugabyteGenerate yb-voyager release notes from a commit range, formatted like the YugabyteDB Voyager release-notes docs page. Use when the user asks to draft release notes, summarize a release, or write what's-new entries for a voyager version.
explain-plan-analyzer
by yugabyteAnalyzes PostgreSQL (and YugabyteDB) EXPLAIN / EXPLAIN ANALYZE query plans to identify performance issues. Use this skill whenever a user pastes an explain plan, asks to analyze a query plan, mentions seq scans or full table scans, wants index recommendations, or asks why a query is slow. Trigger even if the user just says "look at this plan", "what's wrong with this query", or pastes raw EXPLAIN output.
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.