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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validate-wordcount
by rayyan-41Validate a note's word count against template-specific minimums. Delegates actual counting to word_count.ps1 (which correctly strips YAML frontmatter), then compares against the provided minimum threshold. Outputs WORDCOUNT_PASS or WORDCOUNT_FAIL with deficit information. Used by the weaver during Stage 5 of the note creation pipeline. Template minimums — empire/biography 1500, geopolitical 5000, fiqh 8000, aqeedah 3000, cs/ai 4000, notebooklm 4000, general 1000.
word-count
by rayyan-41Count words in a markdown file with correct YAML frontmatter exclusion. Uses a proper two-delimiter YAML parser to strip the frontmatter block (date, status, tags, note properties) before counting body words. Returns a single integer. Used by validate_wordcount.ps1 and directly by agents for word count checks. Fixes the legacy single-pass heuristic that inflated counts by including frontmatter lines.
generate-index
by rayyan-41Generate a vault-wide index of all notes with metadata. Scans every .md file in the De Anima vault, extracts YAML frontmatter (date, status, tags, note), and outputs a structured index. Supports domain filtering, JSON or text output, orphan note detection, and domain note counts. Useful for duplicate detection before note creation, technician audit cross-referencing, vault health snapshots, and pre-computed tag caching for large vaults.
update-pipeline-state
by rayyan-41Track note creation pipeline progress via a JSON state file in _tmp/. Writes or updates the pipeline state after each stage (preflight, yolo, weaver, tagger, formatter, linker) completes, starts, or fails. Enables crash recovery so an interrupted pipeline can resume from the last completed stage. State file is cleaned up by cleanup_chunks after final assembly.
validate-tags
by rayyan-41Validate a tag line against the De Anima canonical tag registry. Checks for exactly one domain tag, exactly one category tag, category-domain compatibility, minimum 3 topic tags (max 10), presence and position of ai-generated tag, and no duplicates. Accepts YAML inline, comma-separated, hash-prefixed, and bracket-wrapped tag formats. Used by the formatter agent in the post-note pipeline to enforce tag conformance.
vault
by rayyan-41Load the De Anima vault context, activate the orchestrator persona, and confirm all 11 agents are ready.
verify-chunks
by rayyan-41Verify YOLO chunk files exist in _tmp/ before weaver assembly. Checks that all expected chunk files (slug_chunk_01.md through slug_chunk_NN.md) are present. Supports two modes — verify (existence check only, returns ALL_PRESENT or MISSING list) and read (verifies AND returns concatenated chunk content with markers). Mandatory gate before weaver assembly to prevent incomplete notes.
write-manifest
by rayyan-41Write a pre-flight manifest JSON to _tmp/ listing all expected YOLO chunk filenames and their headings. Must be called at the start of YOLO generation (Stage 2 pre-flight gate) before any chunks are written. Creates the _tmp/ directory if needed. The manifest enables verify_chunks.ps1 to validate completeness and cleanup_chunks.ps1 to locate all artifacts for deletion.
audit-skill-sync
by rayyan-41Audit vault agent files for skill reference integrity. Checks that domain agents reference required centralized skills (yolo_generation_protocol, obsidian_yaml_enforcer), detects banned legacy patterns (embedded YOLO prompts, old headers), and verifies all skill path references in architecture docs resolve to existing files. Use for vault health checks, post-migration validation, and CI-style sync audits.
cleanup-chunks
by rayyan-41Delete YOLO chunk files, manifest JSON, and pipeline state JSON from the _tmp/ directory after weaver assembly. Called after a note is saved to the vault to clean up temporary generation artifacts. Handles partial cleanup gracefully.
count-citations
by rayyan-41Count and validate inline citations in a note. Scans for [N]-style inline citations in the note body, cross-references them against the
get-related-notes
by rayyan-41Find related notes by tag overlap for wikilink insertion. Scans the entire De Anima vault, compares each note's tags against provided core and supporting tags, and returns a ranked list of backlink candidates. Enforces the formatter link policy with primary (>=2 shared core tags) and secondary (>=1 core tag + same category) match tiers. Used by the linker agent in the post-note pipeline to discover policy-valid wikilink targets.
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.