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...
portfolio-deal-linker
by ScientiaCapitalAuto-update GTME portfolio when HubSpot deals close. Links deal outcomes (won/lost, revenue, cycle time) to the skills, automations, and outreach that influenced them — building VP BD transition evidence automatically. Runs daily at 7am CST or on-demand. Use when: 'portfolio update', 'deal closed', 'link deal to portfolio', 'gtme evidence', 'what did I influence', 'career evidence', 'transition tracker'.
portfolio-artifact
by ScientiaCapitalAuto-extract GTME metrics from work sessions. Lines shipped, bugs fixed, PRs merged, cost per feature. Weekly digests and executive summaries. Use when: capture metrics, portfolio report, what did I ship, weekly summary.
challenger-sale
by ScientiaCapitalThe Challenger Sale methodology — Teach-Tailor-Take Control framework for B2B sales. Use when: challenger sale, commercial teaching, constructive tension, reframe, teach tailor take control, insight selling, commercial insight, challenger rep.
agent-capability-matrix
by ScientiaCapitalMap task types to the best agent, skill, model, and fallback. Route any task to the right tool. Use when: which agent, route task, agent for this, best agent, capability matrix.
callable-lead-count
by ScientiaCapitalDaily callable lead inventory with ATL/BTL breakdown. Health check for 50+ daily dials.
personalization-at-scale-skill
by ScientiaCapitalGenerate unique personalized first lines for hundreds of prospects using company news, LinkedIn activity, and mutual connections. Saves 10+ hours of manual research per campaign. Use when you need personalized outreach at volume.
langgraph-agents
by ScientiaCapitalMulti-agent systems with LangGraph - supervisor/swarm/handoff/router patterns, state coordination, Deep Agents, guardrails, testing, observability, deployment. Use when building multi-agent workflows, coordinating agents, or need cost-optimized orchestration. Uses Claude, DeepSeek, Gemini (no OpenAI).
ibkr-api-skill
by ScientiaCapitalInteractive Brokers (IBKR) API integration for portfolio management, account queries, and trade execution across multiple account types (Roth IRA, personal brokerage, business). Use when the user mentions IBKR, Interactive Brokers, IB Gateway, TWS API, Client Portal API, brokerage API, portfolio positions, account balances, placing trades via API, multi-account trading, IRA trading restrictions, or wants to build/debug code that connects to Interactive Brokers. Also triggers on "ib_async", "ib_insync", "ibapi", or any IBKR endpoint reference.
meeting-intelligence-system-skill
by ScientiaCapitalAnalyze meeting transcripts to extract decisions, action items, blockers, sentiment, and generate follow-up emails. Use when user provides meeting notes, transcripts, or recordings and needs structured summaries or action tracking.
never-split-the-difference
by ScientiaCapitalChris Voss FBI negotiation framework — tactical empathy, calibrated questions, mirroring, labeling, and the Ackerman model for B2B sales and deal negotiation. Use when: negotiation, tactical empathy, calibrated questions, mirroring, labeling, accusation audit, ackerman, deal negotiation, price negotiation, never split the difference.
sales-revenue
by ScientiaCapitalEpiphan Video B2B sales - video capture/streaming lead qualification, pipeline metrics, MEDDIC discovery, and demo execution for Pearl devices, EC20 PTZ, and Epiphan Connect. Use for lead scoring, cold outreach to Higher Ed/Government/Corporate AV, and pipeline reviews.
sales-methodology-implementer-skill
by ScientiaCapitalImplement proven sales methodologies (MEDDIC, BANT, Sandler, Challenger, SPIN) across your team. Generate framework-specific questions, score deals, train reps, and enforce consistent qualification. Use when implementing or optimizing sales processes.
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.