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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socratic-method
by pantheon-orgRefine vague, complex, or high-stakes prompts through Socratic dialogue — surfaces hidden assumptions, probes reasoning, and iterates toward clarity before committing to an implementation.
sci-data-extractor
by pantheon-orgExtract structured data from scientific literature PDFs using AI-powered OCR and LLM analysis. Supports enzyme kinetics, experimental results, and literature review templates. Use when researchers need to parse tables, charts, or text from paper PDFs into Markdown or CSV. Triggers: extract data from PDF, scientific data extraction, parse paper tables, enzyme kinetics extraction, batch PDF extraction, literature data, research data from paper, OCR scientific paper, convert PDF to structured data.
load-context
by pantheon-orgResume session from CONTEXT-llm.md. Use when resuming work, loading saved context, continuing a previous session. Triggers include "load context", "resume session", "continue where I left off".
google-scholar-search
by pantheon-orgSearch Google Scholar for academic papers and author profiles. Use when discovering papers to triage — by keyword, author, or year range. Returns titles, authors, abstracts, and links. Triggers: search papers, find papers, google scholar, search scholar, find author, author profile, literature search, paper discovery, search academic literature.
pubmed-search
by pantheon-orgSearch and analyze biomedical literature from PubMed using the free E-utilities API. Use when researching medical topics, discovering clinical papers, fetching article metadata by PMID, performing deep paper analysis, or downloading open-access PDFs from PubMed Central. Triggers: pubmed search, search biomedical literature, find medical papers, PMID lookup, pubmed metadata, clinical literature search, biomedical research, life sciences papers, PMC download, ncbi search.
challenge
by pantheon-orgChallenge, push back, play devil's advocate on AI output. Use when: challenge this, are you sure, push back, prove it, what if you're wrong, devil's advocate, stress test, poke holes, second opinion, sanity check, too confident, really?, question this decision. Subcommands: anchor (committed too fast), verify (facts wrong?), framing (wrong problem?), deep (full devil's advocate in separate context).
sci-hub-search
by pantheon-orgSearch and download academic papers through Sci-Hub by DOI, title, or keyword. Supports PDF download, metadata extraction, and automatic mirror detection via CrossRef integration. Use when a paper is behind a paywall and you have the DOI or title. Triggers: sci-hub, download paper, fetch paper, academic paper download, paper by DOI, paper access, paywall bypass, retrieve paper, get full text, paper download.
biome-complete
by pantheon-orgComplete Biome toolchain guidance for real repository workflows. Use when users ask to configure biome.json, run lint or format commands, migrate from ESLint or Prettier, tune rule severity, fix formatter drift, or replace mixed ESLint+Prettier pipelines with Biome-only workflows.
ansible-validator
by pantheon-orgComprehensive toolkit for validating, linting, testing, and automating Ansible playbooks, roles, and collections. Use this skill when working with Ansible files (.yml, .yaml playbooks, roles, inventories), validating automation code, debugging playbook execution, performing dry-run testing with check mode, or working with custom modules and collections.
scholar-evaluation
by pantheon-orgSystematically evaluate scholarly and research work using the ScholarEval framework. Use when assessing academic papers, research proposals, literature reviews, or scholarly writing for quality, rigor, and publication readiness. Triggers: evaluate paper, scholar evaluation, research quality assessment, peer review scoring, publication readiness, academic paper review, rate research quality, ScholarEval.
skill-quality-auditor
by pantheon-orgEvaluate, score, and remediate agent skill collections using a 9-dimension quality framework (Knowledge Delta, Mindset, Anti-Patterns, Specification Compliance, Progressive Disclosure, Freedom Calibration, Pattern Recognition, Practical Usability, Eval Validation). Performs duplication detection, generates remediation plans with T-shirt sizing, enforces CI quality gates, validates artifact conventions, tracks score trends, and ensures tessl registry compliance. Use when evaluating skill quality, auditing SKILL.md files, scoring agent skills, generating remediation plans, detecting duplicate skills, validating skill format, enforcing quality gates, optimizing for A-grade publication, comparing audit baselines, batch skill assessments, or checking tessl compliance. Triggers: 'check my skills', 'skill audit', 'improve my SKILL.md', 'quality check', 'A-grade scoring', 'quality gates', 'eval validation', 'audit all skills', 'remediation plan', 'skill judge', 'dimension scoring'.
skill-quality-auditor
by pantheon-orgEvaluate, score, and remediate agent skill collections using a 9-dimension quality framework (Knowledge Delta, Mindset, Anti-Patterns, Specification Compliance, Progressive Disclosure, Freedom Calibration, Pattern Recognition, Practical Usability, Eval Validation). Performs duplication detection, generates remediation plans with T-shirt sizing, enforces CI quality gates, validates artifact conventions, tracks score trends, and ensures tessl registry compliance. Use when evaluating skill quality, auditing SKILL.md files, scoring agent skills, generating remediation plans, detecting duplicate skills, validating skill format, enforcing quality gates, optimizing for A-grade publication, comparing audit baselines, batch skill assessments, or checking tessl compliance. Triggers: 'check my skills', 'skill audit', 'improve my SKILL.md', 'quality check', 'A-grade scoring', 'quality gates', 'eval validation', 'audit all skills', 'remediation plan', 'skill judge', 'dimension scoring'.
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.