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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session-bookmarks
by voxpelliThis skill should be used to save URLs or links found this session as Raindrop bookmarks — triggers: 'bookmark URLs from this session', 'save links we found', 'save notable URLs to Raindrop', 'save session URLs', 'raindrop bookmarks from session'. Scans the current conversation for high-signal URLs discovered during research, previews 1-3 bookmark candidates, and creates them in the AI-bookmarked Raindrop collection after user approval. This is URL/link bookmarking to Raindrop — to capture insights, decisions, or lessons into the Basic Memory knowledge graph instead, use session-reflect.
raindrop-triage
by voxpelliThis skill should be used when the user asks to 'triage unsorted bookmarks', 'clean up raindrop inbox', 'sort unsorted', 'organize bookmarks', 'raindrop triage', 'process bookmark backlog', 'promote triaged bookmarks', 'classify triaged', 'raindrop cleanup', 'deduplicate bookmarks', 'find duplicate bookmarks', 'tag unsorted', 'process raindrop inbox'. Fetches unsorted Raindrop bookmarks, deduplicates by normalized URL, detects research bursts, clusters by theme, proposes vocabulary-grounded tags, cross-references burst topics against Basic Memory, then moves approved bookmarks to AI-triaged. A second invocation with --promote classifies AI-triaged items into AI-sorted (default), AI-gems (golden), AI-archive, or AI-attention.
tag-sync
by voxpelliThis skill should be used when the user asks to 'manage tag vocabulary', 'update raindrop tags', 'sync tag vocabulary', 'curate tags', 'refresh tags file', 'rebuild tag vocabulary', 'what tags should I use', 'tag reference', 'raindrop tag vocabulary', 'create tags reference', 'tag inventory', 'tag sync'. Fetches the user's Raindrop tags, selects the top N by usage count, adds one-line characterizations, groups them by cluster, and writes or syncs the vocabulary file at ~/.claude/references/raindrop-tags.md.
package-intel
by voxpelliThis skill should be used when the user asks to 'research package', 'package intel', 'what does [npm-pkg] do', 'add package to knowledge graph', 'enrich [pkg]', when adding depends_on [[npm-*]] relations, 'research crate', 'what does [crate] do', 'crate intel', 'rust package', 'pypi package', 'python package', 'go module', 'golang package', 'composer package', 'php package', 'ruby gem', 'gem intel'. Researches a package using seven-source enrichment (DeepWiki, Context7, Tavily, Raindrop, Readwise, changelog, Socket) and creates/updates a structured Basic Memory note with post-write cross-linking. Supports npm, Rust crates, Go modules, PHP Composer packages, Python PyPI packages, and Ruby gems.
tool-intel
by voxpelliThis skill should be used when the user asks to 'research a homebrew formula', 'brew intel', 'what does [brew-tool] do', 'research a cask', 'cask intel', 'what does [cask] do', 'research a GitHub Action', 'action intel', 'what does [action] do', 'research a docker image', 'docker intel', 'what does [docker image] do', 'research a VSCode extension', 'vscode intel', 'what does [extension] do', 'research a gh extension', 'research a gh CLI extension', 'gh extension intel', 'what does [gh-extension] do', 'research a Claude Code plugin', 'plugin intel', 'research an agent skill', 'skill intel', 'what does [plugin/skill] do', 'add tool to knowledge graph', 'enrich [tool]'. Researches a developer environment or CI/CD tool using five-source enrichment (DeepWiki, Tavily, Raindrop, Readwise, changelog) and creates/updates a structured Basic Memory note with post-write cross-linking. Supports Homebrew formulae (brew:), Homebrew casks (cask:), GitHub Actions (action:), Docker images (docker:), VSCode extensions (vscode:)
people-intel
by voxpelliThis skill should be used when the user asks to 'research person', 'person intel', 'people intel', 'who is [person]', 'who created [project]', 'who maintains [package]', 'add person to knowledge graph', 'enrich person note', 'update person note', 'document [person]', 'create person note for [name]'. Researches a person using five-source enrichment (Basic Memory, Raindrop, Readwise, Tavily, DeepWiki) and creates/updates a structured Basic Memory person note with post-write cross-linking.
session-reflect
by voxpelliThis skill should be used when the user asks to 'reflect on this session', 'save insights to memory', 'capture what we learned', 'commit this to memory', 'save decisions to Basic Memory', 'write up what we did', 'preserve what we found', 'save insights from this conversation', 'save what was discovered', 'capture session learnings', 'session reflection'. Reviews the current conversation, extracts durable insights (decisions, lessons, gotchas, patterns), previews proposed captures grouped by target note, and writes to Basic Memory after user approval. This captures durable insights as Basic Memory notes — to save URLs or links found this session as Raindrop bookmarks instead, use session-bookmarks.
vp-note-quality
by voxpelliReference checklist for Basic Memory note quality — rules preventing self-referential content in subject-domain notes (the fourth-wall anti-pattern). Preloaded into knowledge-maintainer and knowledge-gardener agents via the skills frontmatter field.
sibling-sync
by voxpelliBilateral SYNERGY/UPSTREAM reconciliation across sibling projects. Use when the user wants to sync sibling SYNERGY/UPSTREAM files, compare both sides to surface drift, find reciprocation gaps (entries here but not there, or vice versa), flag stale-aligned rows, detect status drift across sides, surface friction the sibling tracks ABOUT this project (their UPSTREAM-<this-project>.md), or apply a reciprocation batch with --auto-reciprocate. Workflow 3 covers two UPSTREAM pairing modes: shared third-party dependencies AND reciprocal sibling-friction pairs (UPSTREAM-<sibling>.md here ↔ UPSTREAM-<this-project>.md there). NOT for logging entries on this side (use /synergy-tracker workflow 1 (Log a synergy entry)) — sibling-sync compares both sides without writing by default. NOT for upstream → project drift (use /vendor-sync); sibling-sync handles peer-to-peer drift between sibling vp-* projects. Trigger phrases: 'sibling sync', 'compare siblings', 'sync sibling', 'reconcile siblings', 'reciprocation gap', 'sync dr
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.