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...
blast-radius-check
by bordenetBlast radius analysis - search for ALL usages before modifying any existing code. Prevents breaking unrelated consumers by scoping impact before scoping fix.
todo-management
by bordenetUse when capturing tasks, tracking work, triaging priorities, querying task history, or executing multi-step plans.
expert-interviewer
by bordenetUse when extracting domain knowledge from a user through structured interviewing to produce a written artifact (wiki page, reference doc, problem space overview). NOT for feature design — use brainstorming for that.
branch-flow-gate
by bordenetTrusted-advisor gate for branch and PR hygiene. Auto-invokes when the user mentions creating a branch, opening a PR, shipping/promoting/deploying, cherry-picking, rebasing, hotfixing, or back-syncing. Suggests and explains in-script (preflight always exits 0) and writes a .branch-flow-cleared sentinel that pre-push Gate 3 hard-consumes on pushes to dev/staging/main. Three escape hatches: per-branch ack file (touch .git/base-advisory-ack-<branch>), GIT_BASE_OVERRIDE=1 env var, or use an exempt prefix (hotfix/, release/, backport/, tagged-release/) which is exempt from the base-alignment advisory and typically does not target dev/staging/main. Multi-team config via .git-guidance.yml (currently only default_base is read). Uses git first-parent chain to verify branch base (not naive merge-base). Advises on retry-suffix branches (-vN), back-sync/mirror naming, server-regex compliance, anti-leak fixtures, and loop-on-identical-error retries.
superpowers-help
by bordenetDynamically enumerates ALL installed skills at runtime, distinguishing superpowers (auto-triggered) from explicit skills. Never stale — always reflects current installation.
screenshot
by bordenetVisual input bridge. Grabs the N most recent screenshots from the configured folder and dispatches to an intent-driven action. Supports fix (with project context), explain, compare, spec, do-this, recap, and free-form. Cross-platform folder discovery with auto-populate to ~/.codex/.env.
todo-archive
by bordenetLow-level archive engine for completed tasks in TODO.md. Companion to todo-management; routine housekeeping should usually go through todo-maintenance.sh.
issue-link-verification
by bordenetUse when adding URLs to issue descriptions or comments. Verifies all links before posting to prevent broken references.
professional-language-audit
by bordenetHARD GATE — Scans content for profanity and unprofessional language before publishing to wiki or committing user-facing documentation.
dispatching-parallel-agents
by bordenetUse when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
state-consistency-investigator
by bordenetSpecialized investigator for diagnosing state consistency failures: replication lag, cache staleness, event ordering issues, cross-service data divergence, and eventual consistency bugs. Dispatched by debug-conductor.
wiki-markdown-structure-gate
by bordenetDeterministic structural markdown gate for wiki publishing. Catches malformed tables, escaped wiki-link artifacts, unbalanced code or callout fences, heading hierarchy defects, and missing TOC on manual-TOC platforms with 4+ headings before publish.
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.