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...
ck-find-skills
by tuannguyen-everfitHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
ck-gkg
by tuannguyen-everfitSemantic code analysis with GitLab Knowledge Graph. Use for go-to-definition, find-usages, impact analysis, architecture visualization. Supports Ruby, Java, Kotlin, Python, TypeScript/JavaScript.
ck-markdown-novel-viewer
by tuannguyen-everfitView markdown files with calm, book-like reading experience via HTTP server. Use for long-form content, documentation preview, novel reading, report viewing, distraction-free reading.
ck-deploy
by tuannguyen-everfitDeploy projects to any platform with auto-detection. Use when user says "deploy", "publish", "ship", "go live", "push to production", "host this app", or mentions any hosting platform (Vercel, Netlify, Cloudflare, Railway, Fly.io, Render, Heroku, TOSE, Github Pages, AWS, GCP, Digital Ocean, Vultr, Coolify, Dokploy). Auto-detects deployment target from config files and docs/deployment.md.
rk-copy-function
by tuannguyen-everfitCopy a function from the everfit-api repo into the current project with the minimum code required to run. Triggers on 'copy function', 'copy <name> from everfit-api', 'port function', 'extract function with dependencies'. Recursively traces imports/requires and copies only the symbols actually used — no dead code, no unused exports.
rk-ef-daily-report
by tuannguyen-everfitGenerate an Everfit daily standup report from Jira worklogs and post it as a reply into a Slack thread you provide. Pulls cards you logged work on the target date (DONE / PROGRESS CHANGED) plus your open-sprint cards (PLAN FOR TODAY), derives progress % from time tracking, then drafts the report for your review before replying into the thread. Triggers on: 'daily report', 'standup', 'daily', 'EOD report', 'báo cáo ngày', 'wrap up'.
rk-ef-ship
by tuannguyen-everfitCommit + push the current Everfit feature branch and open a PR targeting develop in one shot. Parses the branch (dev_<sprint>.<type>/<CARD-ID>-<slug>) to build the commit subject `<type>(<feature>): <CARD-ID> <slug>`, asks for the feature scope, pushes with upstream tracking, opens the PR, then auto-chains rk:ef-pr-description to fill the body. Triggers on: 'ship', 'commit and push', 'create PR', 'tạo PR', 'commit + PR', 'push and PR'.
rk-ef-pr-comment
by tuannguyen-everfitPost selected findings from /rk:code-review as inline PR review comments anchored to exact file:line locations. Use AFTER a code review when the user wants the Important (or chosen) items pushed to GitHub as inline comments. Triggers on: comment to pr, post review inline, push findings to pr, drop comments on pr.
rk-ef-pr-description
by tuannguyen-everfitGenerate full PR descriptions in English (default) from code changes + Jira card link. Produces Everfit-style markdown (Summary, Ticket, What changed, Test plan, Rollout). Pass --lang=vi for Vietnamese. Triggers on: 'pr description', 'tạo description PR', 'write PR body', 'generate PR description', 'commit description from jira'.
rk-slack-pr-review
by tuannguyen-everfitReview a PR linked inside a Slack message, then act on the result end-to-end. Given a Slack message URL (whose text contains a GitHub PR link), run rk:code-review on that PR; if it surfaces Critical/Important findings, post them inline via rk:pr-comment and reply in the Slack thread '<@author> Please check my comments'; otherwise comment 'LGTM!' on the PR and reply '<@author> approved'. Fully automatic — no confirmation gate. Triggers on: 'review pr from slack', 'review slack pr link', 'check pr in this slack message', 'review code từ link slack'.
rk-sync-from-everfit
by tuannguyen-everfitCompare files between a fork repo (e.g. metric-service, file-service) and the parent everfit-api repo, then list everfit-api commits that should be cherry-picked. Triggers on 'sync from everfit-api', 'compare metric-service vs everfit-api', 'tìm commit cần cherry-pick', 'check missing commits', 'find cherry-picks', 'so sánh code everfit-api metric-service'. Dual-channel discovery: blame on shared file lines + directory scan for new-file commits and blame-shadowed commits. Skips identical files; dedupes by git patch-id; drops merge commits and commits already in target; runs git apply --check to label each candidate 'clean' or 'manual check'.
rk-update-env
by tuannguyen-everfitSync the default.env file of an everfit-style Node.js repo (everfit-api, metric-service, file-service, etc.) with env vars actually used in the code. Triggers on 'update env', 'update default.env', 'sync env', 'cập nhật default.env', 'thiếu env', 'check missing env'. Scans common/config/*.js Joi schemas + greps process.env.* across the codebase, then adds missing keys (with Joi defaults if available) and reports orphans without deleting.
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.