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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redundancy-consultation
by mohitagw15856Structure a redundancy consultation process and draft key communications. Use when asked to plan a redundancy process, write a redundancy letter, structure a consultation, or manage a reduction in force. UK employment law focus. Always recommend qualified HR/legal advice before proceeding.
diskriminierung-agg-beschwerdestelle
by KlotzketteBerliner Start-up-HR: AGG-Beschwerdestelle: Beschwerde aufnehmen, prüfen, Maßnahmen wählen, Fristen und Vertraulichkeit sichern. Geführter HR-mit Datenschutzfilter, Arbeitsrechtsrouting, Payroll-/DATEV-Schnittstelle, Chef-Briefing und nächstem konkretem Schritt im Startup-HR Berlin: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
retention-risk-rollenrechte-hr-sbv
by KlotzketteBerliner Start-up-HR: Retention-Risk-Map: Kündigungsrisiko, Schlüsselpersonen, Gehaltsband, Konflikte, Entwicklung und zulässige Datengrundlage. Geführter HR-mit Datenschutzfilter, Arbeitsrechtsrouting, Payroll-/DATEV-Schnittstelle, Chef-Briefing und nächstem konkretem Schritt im Startup-HR Berlin: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
argument-crystallization
by yogsoth-aiDistill the strongest arguments from each perspective through Argument Delphi or Dialectical Delphi methods.
disagreement-cartography
by yogsoth-aiMap the structure of disagreement across perspectives using Policy Delphi, Argument Delphi, or SAST methods.
resurrection-advocacy
by yogsoth-aiArgue for rejected candidates using Devil's Advocacy, Dialectical Inquiry, and Adversarial Collaboration to ensure elimination was justified.
negotiation
by NeverSightExpert negotiation coaching combining Harvard, Kellogg, Wharton, HBS, and FBI methodologies. Use when: (1) preparing for any negotiation (salary, contracts, deals, disputes), (2) during a negotiation for real-time tactics, (3) analyzing failed negotiations, (4) coaching someone through difficult conversations, (5) resolving conflicts. Covers: BATNA analysis, MESOs, tactical empathy, anchoring, reframing, and more.
influence-and-negotiation
by samberInfluence and negotiation toolkit for any interaction requiring another person's agreement, even when not framed as 'negotiation'. Covers: B2B sales, salary review, collective bargaining/unions, hard 1:1s, decision announcements, mediation, cross-cultural deals, recruitment, reaching out to a manager, CFO, customer, vendor, or colleague, responding to feedback, headcount requests, declining, pushing back on scope, justifying a delay, explaining a decision, raising a concern, getting alignment. Apply when preparing, live, or drafting any diplomatic message. Triggers: coaching prompts ('they just said X', 'what do I say', 'draft a reply'); counterparty cues (buyer, customer, champion, procurement, RFP, sponsor, HR, union, CHRO, ExCo, candidate, counter-offer, partner, peer); situation cues (pushback, refusal, ghosted, no-decision, escalation, fixed budget, MFN, raise, comp band, strike, layoff, recadrage, expectation reset, M&A, BATNA, objection, concession, anchor, mirroring).
resolve-dispute
by DheerGResolves stuck review findings via a put-up-or-concede exchange. Invoked by the facilitator when a finding survives arbitration without new evidence.
negotiation
by aAAaqwqTactical negotiation framework based on Chris Voss's "Never Split the Difference." Use when preparing for negotiations, during live negotiation scenarios, analyzing counterpart behavior, crafting responses to difficult conversations, handling objections, salary/contract negotiations, or when asked about negotiation techniques like mirroring, labeling, calibrated questions, or the Ackerman method.
active-listening
by TibsfoxActive listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.
openclaw-0270-policy-scoped-data-mediation
by FDU-INSCollab Privacy Preserving Data Broker. Use when work requires policy-scoped data mediation for Collaboration and Negotiation with guardrails, traceable execution, and measurable outcomes.
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.