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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dependency-audit
by databricks-industry-solutionsAudit npm and Python dependencies across all Databricks Apps in a workspace. Use when checking for malicious packages, generating dependency inventories, or investigating supply chain risks. Triggers on dependency audit, npm audit, package audit, supply chain, malicious package, dependency inventory.
many-model-forecasting
by databricks-industry-solutionsKickstart Many Models Forecasting (MMF) projects on Databricks — explore data, profile series, configure clusters, run forecasting pipelines, and evaluate results.
dbxmetagen-development
by databricks-industry-solutionsDevelopment guidance for the dbxmetagen project -- AI-powered metadata generation for Databricks Unity Catalog. Use when working on dbxmetagen source code, tests, DDL generation, metadata review workflow, semantic layer, FK prediction, ontology, the FastAPI+React dashboard app, or deployment via Databricks Asset Bundles. Triggers on dbxmetagen, metadata generation, DDL, review workflow, knowledge base, knowledge graph, Genie builder, metric views.
safety-assessment
by databricks-industry-solutionsBased on a compound, get its safety info. Assess compound safety profile including toxicity, hazard classifications, and regulatory information using PubChem and PubMed.
adme-assessment
by databricks-industry-solutionsBased on a compound, get its ADME and other properties. Assess compounds for ADME (Absorption, Distribution, Metabolism, Excretion) characteristics, chemical properties, and drug-likeness using PubChem. Use when the user wants to evaluate compound suitability as a lead candidate. Triggers include requests like "assess drug-likeness for [compound]", "evaluate ADME for [CID]", "ADME assessment", "check Lipinski rules for [compound]", "molecular properties of [drug]", or "is [compound] a good lead candidate". Accepts PubChem CIDs, compound names, SMILES, or InChI as input.
target-identification
by databricks-industry-solutionsBased on a disease, identify therapeutic targets. Use this skill when users ask to find drug targets, therapeutic targets, or druggable genes for any disease or condition. Triggers include queries like "find druggable targets for [disease]", "what are therapeutic targets for [disease]", "identify drug targets for [condition]", or any request to discover targetable genes/proteins for a disease. The skill uses Open Targets Platform for disease-target associations, clinical precedence, and tractability data, combined with PubMed for supporting mechanistic evidence. Outputs a ranked table of top 10 targets with evidence summaries.
hit-identification
by databricks-industry-solutionsBased on a target, get its associated drugs. Identify small molecule hits for therapeutic targets by querying Open Targets and PubChem. Use when the user provides a gene symbol (e.g., EGFR, BRAF, KRAS) or protein name and wants to find known compounds, drugs, or chemical matter with activity against that target. Returns compound identifiers, names, bioactivity data, clinical trial phases, mechanism of action summaries, and supporting literature with PubMed links. Triggers include requests like "find hits for [target]", "what compounds bind [gene]", "identify drugs targeting [protein]", "small molecules for [target]", or "hit identification for [gene symbol]".
deploy
by databricks-industry-solutionsDeploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
validate-incremental-sync
by databricks-industry-solutionsValidate that ADME domain tables (wellbore, reservoir, rock_and_fluid) correctly implement CDC incremental sync — offset tracking, watermark filtering, and pagination termination.
test-and-fix-connector
by databricks-industry-solutionsRun the AdmeOsduLakeflowConnect pytest suite, diagnose failures, and fix the connector or simulator until everything passes. Branches on mode={simulate|live}.
authenticate-source
by databricks-industry-solutionsSet up authentication for the ADME OSDU connector — get an Azure bearer token and write dev_config.json for live testing.
self-review-connector
by databricks-industry-solutionsMechanical audit of the ADME OSDU connector — implementation correctness, test coverage, artifacts completeness, and security. Produces a scored report.
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.