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...
enrich-ontology
by cartography-cncfMap a Cartography node into the Ontology system using semantic labels (UserAccount, DeviceInstance, Tenant, Database, ObjectStorage, FileStorage) or canonical nodes (User, Device). Use when the user asks to add ontology mapping, expose a node as a semantic label, normalise identity / device data across providers, enable cross-module queries, or wire `_ont_*` properties.
troubleshooting
by cartography-cncfDiagnose and fix common Cartography intel-module errors — `ModuleNotFoundError`, `PropertyRef validation failed`, `GraphJob failed`, missing relationships, MatchLink misses, cleanup deleting too much, slow queries, ignored custom schema fields, key errors during transform. Use when the user reports an error while developing or running a Cartography module.
promote-ontology-relationship
by cartography-cncfPromote provider-specific relationships to a canonical cross-provider ontology edge using the WORKLOAD_PARENT pattern (a parallel CartographyRelSchema with the canonical rel_label, the old edge kept and deprecated, plus a RelConstraint enforced by the CI guard). Use when the user asks to "propagate X->Y relationship to the ontology level", unify/normalise a relationship label across providers, add a canonical ontology edge (HAS_ROLE, MEMBER_OF, ASSUMES, ENCRYPTED_BY, POINTS_TO, ...), add a RelConstraint, or deprecate/rename an ontology relationship label.
refactor-legacy
by cartography-cncfConvert a legacy handwritten-Cypher Cartography sync (`load_*` / `cleanup_*` JSON jobs) into the modern declarative data model (`load()`, `GraphJob.from_node_schema()`). Use when the user asks to refactor, modernise, migrate, or "clean up" a legacy intel module, or to remove a `cleanup/*.json` job tied to an old `MERGE` query.
add-node-type
by cartography-cncfDefine a new node schema under cartography/models/MODULE_NAME/, including required properties, sub-resource relationships, extra labels, conditional labels, scoped cleanup, and one-to-many transforms. Use when the user asks to add a node type, model a new resource, configure extra Neo4j labels (Identity, Asset, UserAccount, Tenant), or wire scoped vs global cleanup.
add-relationship
by cartography-cncfDefine a `CartographyRelSchema` (standard relationship), one-to-many edge, or `MatchLink` connecting existing nodes. Use when the user asks to add a relationship, link nodes, set a `RESOURCE` / `MEMBER_OF` / `ASSOCIATED_WITH` edge, share a node across modules, or model a composite node from two intel sources.
analysis-jobs
by cartography-cncfAdd a post-ingestion analysis job (JSON Cypher file) to a Cartography module to enrich the graph after sync. Use when the user asks to compute internet exposure, propagate inherited permissions, link Human / canonical ontology nodes, score risk, or add cross-resource analysis after data is loaded.
audit-frameworks
by cartography-cncfAudit Cartography's rules and compliance frameworks under `cartography/rules/data/rules/`. Surfaces TODOs that the schema can now satisfy, per-provider rules that should collapse into one ontology rule, and duplicate detections across frameworks (CIS, ISO 27001, SOC 2, NIST). Use when the user asks to "audit frameworks", "audit rules", "review rule TODOs", "find duplicate rules", "find ontology candidates", "consolidate compliance frameworks", or "map ISO/SOC2 onto CIS".
create-module
by cartography-cncfAuthor a new Cartography intel module end-to-end (entry point, sync GET/TRANSFORM/LOAD/CLEANUP, declarative data model, integration test, schema docs). Use when the user asks to add a new provider, integration, intel module, or service ingestion to Cartography (e.g. "add a new module for service X", "integrate ServiceY", "create a sync for Z API").
create-rule
by cartography-cncfAuthor a Cartography security rule (one or more Cypher Facts plus a Pydantic Finding output model) under `cartography/rules/data/rules/`. Use when the user asks to add a security check, detection, attack-surface query, compliance control, CIS benchmark rule, or cross-cloud detection.
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.