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...
peer-comms
by alto84Use when about to send work to a peer machine (rtxpro6000server / gpuserver1) or check what one is doing. Loads the OAuth-ceremony, tmux protocol, send-keys+C-m, file-not-heredoc rule, phone-home convention, and per-peer quirks. Trigger when typing or thinking "ssh alton@192.168.1.{100,157}" for substantive work; not needed for quick read-only one-shots.
safety-research-workflow
by alto84Guides systematic safety research including literature review, multi-agent research coordination, citation management, evidence validation, and quality assurance. Integrates Memory MCP to persist research findings, methodology patterns, and research iterations across sessions. Use when conducting research studies, coordinating research agents, managing bibliographies, validating research claims, or ensuring research quality.
wiki-reindex
by alto84Nightly Hermes-pattern reindex of an LLM safety research wiki. Regenerates backlinks.json, tag-index.json, orphans.json, broken-links.json, similarity.json, and the bounded state/wiki-state.md. Surfaces orphans and broken links for review. Drop into any scheduler (cron, systemd timer, Windows Task Scheduler, agent harness) that can run python.
safety-research-wiki
by alto84Use when building a pharmacovigilance / drug safety research knowledge base that compounds across hundreds of papers, trials, and signals. Creates a compiled LLM-wiki with mechanism pages, drug pages, adverse event pages, and signal pages, plus an ingest workflow for PubMed / FDA / EMA / internal trial data. Self-contained; works offline; no dependencies on any proprietary source system. Use in an AZ/work environment where you need to accelerate literature review, signal detection, or pre-submission evidence synthesis.
tax-counsel
by alto84Use when Alton asks a tax, structuring, or financial-planning question that requires authority-grounded analysis (not just calculation) — IRC sections, regulations, deductibility tracing, entity character, gain recognition events, tax-deferral strategy. Distinct from `tax-estimate` (which produces numeric estimates) and from `financial-research` rules (which govern trade analysis). Operates in tax-counsel register: issue-spotting, IRAC memos, risk grading, CPA coordination. Not legal advice; analytical support for Jonathan Francis discussions.
travel-planning
by alto84Family trip planning for family of 5 with kids ages 10, 8, and 4
personal-data-gather
by alto84Persistent data collection across Gmail, Calendar, and Drive — updates daily logs and memory snapshots
secrets-via-bitwarden
by alto84Use whenever a credential (password, API key, login) is needed for a service that doesn't already have a per-service token file. Loads the Bitwarden CLI workflow, the sartor-secret wrapper, the locked-vault handling, the per-machine setup, and the migration recipe for known-leaked passwords. Reference secrets by name, never by value.
deep-research
by alto84Multi-source research synthesis on a specified topic with structured argument construction
evidence-based-engineering
by alto84Enforces evidence-based claims, prevents metric fabrication, and ensures honest assessment. Use when making ANY quantitative claim, performance assertion, completion estimate, or quality judgment. Prevents over-promising and fabricated metrics.
evidence-based-engineering
by alto84Enforces evidence-based claims, prevents metric fabrication, and ensures honest assessment. Use when making ANY quantitative claim, performance assertion, completion estimate, or quality judgment. Prevents over-promising and fabricated metrics. Integrates with Memory MCP to store baselines, methods, and lessons for cumulative improvement.
evidence-based-validation
by alto84DEFAULT BEHAVIOR - Enforces anti-fabrication protocols, detects score fabrication, prohibits exaggerated language, and ensures evidence-based claims. AUTOMATICALLY stores validation findings in Memory MCP. Use when analyzing performance, reviewing code quality, assessing systems, or making any claims requiring measurement data.
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.