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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plugin-validator
by jeremylongshoreValidate automatically validates AI assistant code plugin structure, schemas, and compliance when user mentions validate plugin, check plugin, or plugin errors. runs comprehensive validation specific to AI assistant-code-plugins repository standards. Use when validating configurations or code. Trigger with phrases like 'validate', 'check', or 'verify'.
brightdata-prod-checklist
by jeremylongshoreExecute Bright Data production deployment checklist and rollback procedures. Use when deploying Bright Data integrations to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "brightdata production", "deploy brightdata", "brightdata go-live", "brightdata launch checklist".
brightdata-rate-limits
by jeremylongshoreImplement Bright Data rate limiting, backoff, and idempotency patterns. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Bright Data. Trigger with phrases like "brightdata rate limit", "brightdata throttling", "brightdata 429", "brightdata retry", "brightdata backoff".
brightdata-reference-architecture
by jeremylongshoreImplement Bright Data reference architecture with best-practice project layout. Use when designing new Bright Data integrations, reviewing project structure, or establishing architecture standards for Bright Data applications. Trigger with phrases like "brightdata architecture", "brightdata best practices", "brightdata project structure", "how to organize brightdata", "brightdata layout".
brightdata-sdk-patterns
by jeremylongshoreApply production-ready Bright Data SDK patterns for TypeScript and Python. Use when implementing Bright Data integrations, refactoring SDK usage, or establishing team coding standards for Bright Data. Trigger with phrases like "brightdata SDK patterns", "brightdata best practices", "brightdata code patterns", "idiomatic brightdata".
brightdata-security-basics
by jeremylongshoreApply Bright Data security best practices for secrets and access control. Use when securing API keys, implementing least privilege access, or auditing Bright Data security configuration. Trigger with phrases like "brightdata security", "brightdata secrets", "secure brightdata", "brightdata API key security".
brightdata-upgrade-migration
by jeremylongshoreAnalyze, plan, and execute Bright Data SDK upgrades with breaking change detection. Use when upgrading Bright Data SDK versions, detecting deprecations, or migrating to new API versions. Trigger with phrases like "upgrade brightdata", "brightdata migration", "brightdata breaking changes", "update brightdata SDK", "analyze brightdata version".
podium-rag-context-bridge
by jeremylongshoreBridge a live Podium call transcript or webchat turn to an LLM by fetching relevant historical conversation context as a structured RAG bundle — vector search over embedded prior conversations + reranking + live Podium contact lookup, merged under a hard latency budget that keeps the call answerable in real time. Use when wiring a transcription-driven agent loop to an LLM that needs cross-channel customer memory, building the substrate that turns "transcript chunk" into "LLM-ready prompt with context", or hardening an existing RAG pipeline against staleness, reranking noise, PII leakage, token-budget overflow, and missed-the-call latency. Trigger with "podium rag", "podium llm context", "podium transcript to llm", "podium retrieval", "podium vector search", "podium real-time context", "podium agent grounding".
shopify-b2b-wholesale
by jeremylongshoreBuild Shopify Plus B2B features with companies, catalogs, and wholesale pricing. Use when setting up wholesale accounts, creating price lists, or configuring B2B checkout with purchase orders. Trigger with phrases like "shopify b2b", "shopify wholesale", "shopify company api", "shopify price lists", "shopify catalog api".
brightdata-core-workflow-b
by jeremylongshoreExecute Bright Data secondary workflow: Core Workflow B. Use when implementing secondary use case, or complementing primary workflow. Trigger with phrases like "brightdata secondary workflow", "secondary task with brightdata".
brightdata-core-workflow-a
by jeremylongshoreScrape structured data with Bright Data Scraping Browser using Playwright/Puppeteer. Use when scraping JavaScript-rendered pages, SPAs, or sites requiring browser interaction. Trigger with phrases like "brightdata scraping browser", "brightdata playwright", "brightdata puppeteer", "scrape SPA with brightdata", "browser scraping".
brightdata-common-errors
by jeremylongshoreDiagnose and fix Bright Data common errors and exceptions. Use when encountering Bright Data errors, debugging failed requests, or troubleshooting integration issues. Trigger with phrases like "brightdata error", "fix brightdata", "brightdata not working", "debug brightdata".
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.