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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financial-model-review
by NateBJones-ProjectsInvestor-first workflow for reviewing an existing financial model, forecast, or sensitivity analysis. Use for prompts like "review this model", "stress test these assumptions", "what breaks in this forecast", or "is this model decision-ready". Best when you have a spreadsheet export, pasted assumptions, key drivers, and the decision the model supports. Optional Open Brain search and capture can pull prior model context and store the final review memo.
heavy-file-ingestion-claude-code
by NateBJones-ProjectsUse in Claude Code when a user asks to read, analyze, summarize, or extract from a heavyweight file such as PDF, DOCX, PPTX, XLSX, CSV, or TSV. Convert the file into markdown or CSV first with the bundled script, generate a lightweight index, and only spend model tokens on the compressed artifact.
heavy-file-ingestion-codex
by NateBJones-ProjectsUse in Codex when a user asks to read, analyze, summarize, or extract from a heavyweight file such as PDF, DOCX, PPTX, XLSX, CSV, or TSV. Convert the file into markdown or CSV first with the bundled script, generate a lightweight index, and only spend model tokens on the compressed artifact.
heavy-file-ingestion
by NateBJones-ProjectsUse when a user asks to read, analyze, summarize, or extract from a heavyweight file such as PDF, DOCX, PPTX, XLSX, CSV, or TSV. Convert the file into markdown or CSV first, generate a lightweight index, and only spend model tokens on the compressed artifact. Trigger on requests like "read this PDF", "look through this spreadsheet", "summarize this deck", or any time raw file ingestion would waste tokens.
heavy-file-ingestion-claude-desktop
by NateBJones-ProjectsUse in Claude Desktop when a user asks to read, analyze, summarize, or extract from a heavyweight file such as PDF, DOCX, PPTX, XLSX, CSV, or TSV. Avoid raw ingestion of bulky files. Ask for a converted markdown or CSV artifact first, or give the user exact conversion commands to run outside Claude Desktop.
skill-name
by NateBJones-ProjectsExplain exactly when this skill should fire, what inputs it expects, and what problem it solves.
nbj-ob1-agent-memory-openclaw
by NateBJones-ProjectsUse Nate Jones OB1 Agent Memory from OpenClaw with provenance, scope, review, and use-policy discipline.
nbj-ob1-agent-memory-openclaw
by NateBJones-ProjectsUse Nate Jones OB1 Agent Memory from OpenClaw with provenance, scope, review, and use-policy discipline.
ob1-local-http
by NateBJones-ProjectsCapture and search thoughts against a self-hosted Open Brain over plain HTTPS, with no MCP transport involved. Use this skill in environments where Claude Code's MCP feature is disabled or the network blocks remote MCP endpoints, but the brain stack from the companion `local-brain-no-mcp` recipe is reachable on the local network. Triggers: prompts like "remember this", "save that for later", "what did I note about X", "search my brain for Y", "what thoughts touched on Z", or any explicit request to record or recall personal memory.
world-model-diagnostic
by NateBJones-ProjectsTwenty-minute conversational diagnostic for assessing a company's world-model readiness. Use when the user wants to map their company to the right world-model paradigm, identify where the highest-fidelity signal lives, audit the boundary layer between facts and interpretation, flag simulated-judgment exposure, and leave with a first/second/third build sequence. Works in plain chat and compounds when Open Brain search/capture tools are present.
weekly-signal-diff
by NateBJones-ProjectsUse when the user wants a weekly structural diff on AI, software, or another fast-moving market. Starts from 10 suggested categories and 30 suggested companies when no watchlist exists, then adapts the scan using Open Brain memory, current priorities, and prior digests. Best for prompts like "run my weekly signal diff", "what changed this week that matters to me", "track this market", or "turn this week's news into structural shifts". Optional live search upgrade: if OpenRouter access is available, prefer the Perplexity Sonar family for fresh web-grounded retrieval with citations.
meeting-synthesis
by NateBJones-ProjectsWorkflow for turning meeting transcripts or notes into decisions, action items, unresolved questions, risks, and follow-up artifacts. Use for prompts like "summarize this meeting", "extract action items", "what did we decide", or "draft the follow-up". Best when you have notes or a transcript, attendee context, and the reason the meeting happened. Optional Open Brain search and capture can pull surrounding context and store the final synthesis or decisions.
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.