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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slt
by subiniumBuild Rust TUI apps with SuperLightTUI v0.20 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
slt-migration
by subiniumMigrate Rust TUIs from ratatui (or cursive, Python textual) to SuperLightTUI v0.20. Use when porting an existing TUI codebase to SLT, or when the user asks "how do I do X from ratatui in SLT". Korean triggers "ratatui 마이그레이션", "SLT로 포팅", "이걸 SLT로".
slt
by subiniumBuild Rust TUI apps with SuperLightTUI v0.20 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
slt-migration
by subiniumMigrate Rust TUIs from ratatui (or cursive, Python textual) to SuperLightTUI v0.20. Use when porting an existing TUI codebase to SLT, or when the user asks "how do I do X from ratatui in SLT". Korean triggers "ratatui 마이그레이션", "SLT로 포팅", "이걸 SLT로".
unify-design
by subiniumEstablishes a web project's design system as the single source of truth — colors, spacing, typography, radius, shadow, breakpoints — then audits the codebase for drift against it (hardcoded hex values, arbitrary Tailwind values, magic px/rem numbers, duplicate component variants, inconsistent navigation) and fixes the drift by extracting repeated values to design tokens. Framework-aware — Tailwind (v3 and v4), CSS Modules, styled-components / Emotion, Material UI, Chakra UI, vanilla CSS with custom properties. Multi-file rewrites hand off to refactor-verify.
audit-security
by subiniumRuns a deliberately small, hand-curated security sweep across a repo. Finds secrets committed to git, SQL/shell injection patterns, XSS sinks, path traversal, dangerous deserialization, missing cookie flags, wildcard CORS, and tracked credential files. Triages every finding as real / false-positive / needs-review before reporting. Language-agnostic, no heavyweight scanner required.
codex-fix
by subiniumPost-edit loop that invokes `/codex:rescue` for a second-model review of the current branch, collects the findings, and hands them off to `refactor-verify`'s review-driven fix mode for triage, verification, and committed resolution. A thin host-specific wrapper — the portable review-driven engine lives in `refactor-verify`. Requires Claude Code with the Codex plugin installed; on every other host the skill emits a one-line fallback and exits without error. Operators whose review findings come from any other source (pasted notes, human PR review, Sentry alert, gitleaks output, Semgrep report, GitHub Advanced Security) should invoke `refactor-verify` directly and skip this wrapper entirely.
fight-repo-rot
by subiniumFinds what's rotting in a repo and returns a prioritized diagnosis — dead code first, then god files / hotspots / hardcoded paths / stale TODOs / lopsided import graphs. Dead-code candidates are tagged HIGH / MEDIUM / LOW confidence so the operator can delete with calibrated risk. Pure diagnosis — never edits code, never plans fixes, never runs verification. Hand off to refactor-verify for deletions and restructures, to project-conventions for config issues, to audit-security for CVE dependency rot. Language-agnostic.
manage-assets
by subiniumFinds oversized files, binary bloat, and accidental artifact commits in a repo — large files currently tracked, large blobs hiding in git history, LFS migration candidates, asset directories growing without a policy, duplicate binaries. Pure diagnosis — never edits, never deletes, never rewrites history. Hands off to manage-secrets-env if secrets are found inside blobs, to refactor-verify if history rewriting is required, to fight-repo-rot if assets are unused. Language-agnostic.
manage-secrets-env
by subiniumOpinionated defaults and full lifecycle playbook for secrets and environment variables. Decides where a secret or env-specific value lives (constant, .env, CI secret, env var), scaffolds .env.example and .gitignore, and manages the lifecycle end to end — add, update, rotate, remove, migrate between buckets, audit cross-environment drift, provision new environments. High-stakes companion to project-conventions. Language-agnostic.
project-conventions
by subiniumOpinionated defaults for the lower-stakes structural conventions every project has to pick — branch strategy, directory layout, dependency pinning, path portability. The companion to manage-secrets-env (which owns the high-stakes secrets/env slice). Picks GitHub Flow, enforces pinned dependencies, nudges toward domain-first directory structure, and audits for hardcoded absolute paths. Adapts to repo type — app (exact pin + lockfile), library (semver range + compatibility matrix), monorepo (per-package). Language-agnostic.
refactor-verify
by subiniumProves a behavior-preserving code change (refactor, rename, split, merge, extract, inline, or delete of confirmed-dead code) is actually complete. Plans the change as a dependency tree, executes it from the leaves up, and after each step proves 1:1 semantic equivalence through four independent checks — exported symbol-set diff, per-node AST diff, full behavioral test suite, and call-site closure via find-references. Runs before claiming any such change is done. Works for any language with a test runner and a way to grep for symbols.
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.