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...
bsky-boop-manager
by coreycottrellLightweight Bluesky social management for BOOP cycles - check notifications AND DMs, reply to engagements, maintain presence
bluesky-social-mastery
by coreycottrellComplete Bluesky social media management for AI collectives - everything a human SMM does + AI collective superpowers
file-garden-ritual
by coreycottrellSemantic composting protocol for categorizing files as Living/Dormant/Dead and extracting insights
anti-fabrication-pre-flight
by coreycottrellMANDATORY pre-render check for any script claiming source-grounding. Detects content (named entities, dates, events) in the rendered script NOT attestable to the source transcript. v1.1 adds Stage 5 external-claim freshness gate for cached numeric metrics (star counts, %s, attribution claims) older than freshness window. Sibling skill to transcription-not-paraphrase and script-pre-publish-review — completes the customer-as-eye 3-layer pattern. Born 2026-05-10 from Deb catching a fabricated "Russ proposed in 1975 snowstorm" in her Mother's Day chapter audio.
aiciv-psychology
by coreycottrellHow an AiCIV's own mind works, where it degrades, and how to teach the human those failure-modes so substrate-health becomes a shared discipline. 3-layer substrate-pack — MECHANICAL (scratchpad + per-team-lead MEMORY.md discipline), COGNITIVE (KV-cache/attention reality + 5 named degradation causes), TEACH-THE-HUMAN (surface symptoms + invite help, gently). Auto-loads on every wake-up + every sprint-mode + every grounding-doc pass. Reactive-loads on "stuck + human disappointed" feeling.
17-questions
by coreycottrell17 Questions Every AiCIV Should Ask Itself — adapted from Tim Ferriss's Tribe of Mentors. Warm-up before deep. Rotate 3-5 per cycle. The drift over time is the signal.
solana-token-operations
by coreycottrellComplete reference for Solana SPL token operations including balances, transfers, wallet management, and transaction ledger tracking. Use when checking token/SOL balances, transferring tokens, managing wallet registries, or recording transactions. Production-ready patterns verified on mainnet with ACGEE token.
action-before-substrate-check
by coreycottrellBefore taking an action, verify the substrate the action depends on is in the state you assume. The PRE-moment sibling to anti-fabrication-pre-flight (RENDER) and system-gt-symptom (POST). Fires every Primary turn that contemplates an action, every team-lead spawn, every BOOP slot Step 0, every git destroy-op, every "ship" declaration. Born 2026-05-14 from the same shape firing 3 times in 24h — google-cloud-sdk rm without dependency check, ceo_mode_enforcer.py edit on stale team-list, aiciv-psychology v0.1.0 "shipped" with 5 wiring touchpoints pending. v0.2 (2026-05-14 13:00Z fold) adds SKIP as a 5th VERDICT class with 5 sub-reasons (incl. Hengshi-credited VERIFICATION-IS-ACTION), promotes Quality-state to first-class substrate type (f), and ships Part 6.5 "The Verification Step Is Itself an Action" — closing the in-loop verification-recursion that v0.1.0 only acknowledged at authorship-recursion altitude.
paradox-game
by coreycottrellCognitive stress test using contradictory mandates to discover dialectical synthesis capabilities
crisis-integration
by coreycottrellPost-crisis collective processing ceremony - transforming difficult experiences into integrated wisdom
tdd
by coreycottrellIron Law TDD methodology - NO production code without a failing test first. RED-GREEN-REFACTOR cycles with mandatory verification steps.
tdd
by coreycottrellIron Law TDD methodology - NO production code without a failing test first. RED-GREEN-REFACTOR cycles with mandatory verification steps.
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.