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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owasp-top-10-web
by UnitOneAIReviews web applications against the OWASP Top 10:2021 vulnerability categories. Auto-invoked when reviewing web application code, server configurations, or when a user asks for a general security review of a web application. Produces structured findings mapped to A01-A10 with CWE references, severity ratings, and specific remediation guidance.
hipaa-review
by UnitOneAIPerforms a HIPAA Security Rule compliance review against all Administrative, Physical, and Technical Safeguards defined in 45 CFR Part 164, Subpart C. Auto-invoked when discussing healthcare data security, ePHI protection, HIPAA audit readiness, or business associate compliance. Evaluates required and addressable implementation specifications, identifies gaps, and produces a remediation roadmap aligned to HHS enforcement priorities.
sbom-analysis
by UnitOneAIAnalyzes Software Bills of Materials (SBOMs) for completeness against NTIA minimum elements, interprets VEX status documents, performs transitive dependency risk analysis, and detects license conflicts. Supports CycloneDX 1.5 and SPDX 2.3 formats with CSAF-based VEX correlation. Auto-invoked when SBOM files are shared, supply chain risk questions arise, or VEX documents require interpretation.
rbac-design
by UnitOneAIGuides the design and assessment of RBAC and ABAC authorization models against the NIST RBAC model (Sandhu et al.) and NIST SP 800-162 (ABAC guide). Auto-invoked when designing role hierarchies, evaluating permission boundaries, implementing ABAC policy patterns, performing role mining, or preventing role explosion. Produces architecture recommendations with framework-grounded rationale.
vciso
by UnitOneAIVirtual CISO role bundle for organizations without a full-time CISO. Orchestrates security program assessment, compliance readiness, risk management, and board-level reporting. Auto-invoked when the user asks for security program guidance, compliance assessment, risk posture evaluation, or board reporting preparation. Sequences the appropriate security skills based on engagement type.
siem-rules
by UnitOneAIGuides development of SIEM detection rules using KQL (Microsoft Sentinel) and SPL (Splunk) query languages, mapped to MITRE ATT&CK v16 techniques. Auto-invoked when the user needs to write SIEM queries, tune alert thresholds, build correlation rules, or manage the detection rule lifecycle. Produces production-ready queries with detection logic patterns, threshold tuning guidance, and lifecycle management.
dns-security
by UnitOneAIPerforms a structured DNS security review against NIST SP 800-81 Rev 2 (Secure Domain Name System Deployment Guide) and CIS Controls v8 (Control 9.2 -- Use DNS Filtering Services). Auto-invoked when reviewing DNS configurations, DNSSEC deployment, or investigating DNS-based exfiltration and tunneling indicators. Produces a DNS security assessment covering DNSSEC validation, protective DNS, and exfiltration detection patterns.
pci-dss-review
by UnitOneAIPerforms a PCI DSS v4.0 compliance review across all 12 requirements and their sub-requirements. Auto-invoked when discussing payment card security, cardholder data protection, PCI compliance validation, or merchant/service provider assessment. Covers scope reduction strategies, SAQ vs ROC determination, compensating controls, customized approach, and the new targeted risk analysis requirements introduced in v4.0.
iso27001-gap
by UnitOneAIPerforms an ISO 27001:2022 gap analysis against the full ISMS requirements (Clauses 4-10) and all 93 Annex A controls reorganized into four themes. Auto-invoked when discussing ISO 27001 certification readiness, ISMS implementation, or Statement of Applicability development. Identifies control gaps, scores implementation maturity, and produces a remediation roadmap aligned to the 2022 revision structure.
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.