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...
llamactl-qa
by run-llamaPlan and run a design-QA / "taste test" of llamactl changes against a real backend. Cooperatively builds a small matrix of cases worth eyeballing, runs them against a chosen backend (test environment by default, local kind+tilt for new API contract changes), and writes a design-review report to `thoughts/shared/qa/`. Use this for changes to `llamactl` commands, output formats, auth, or control-plane API contracts. For UI/template smoke tests, use `llamactl_browser_test` instead.
retrieve-relevant-information-through-rag
by run-llamaLeverage Retrieval Augmented Generation to retrieve relevant information from a a LlamaCloud Index. Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
classify-files-according-to-specific-rules
by run-llamaInvoke this skill BEFORE implementing any text/document classification task to learn the correct llama_cloud_services API usage. Required reading before writing classification code." Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
extract-structured-data-from-unstructured-files-pdf-pptx-docx
by run-llamaInvoke this skill BEFORE implementing any structured data extraction from documents to learn the correct llama_cloud_services API usage. Required reading before writing extraction code. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
pdf-processing
by run-llamaInvoke this skill BEFORE implementing any text extraction/parsing logic to learn how to use LlamaParse to process any document accurately. Requires llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.
use-llamactl-a-cli-tool-for-llamaagents
by run-llamaUse llamactl to initialize, locally preview, deploy and manage LlamaIndex workflows as LlamaAgents. Required llama-index-workflows and llamactl to be installed in the environment.
liteparse
by run-llamaUse this skill when the user asks to parse, perform multi-format document conversion or spatially extract text from an unstructured file (PDF, DOCX, PPTX, XLSX, images, etc.) locally without cloud dependencies.
llamaparse
by run-llamaUse this skill when the user asks to parse the content of an unstructured file (PDF, PPTX, DOCX...)
llamaparse-mcp
by run-llamaSkill on how to use the LlamaParse MCP tools
install
by run-llamaRun `helm install` (or `helm upgrade --install`) for the LlamaCloud chart, monitor the rollout in real time, surface failing pods immediately, and capture a diagnostic bundle if anything fails. Use when the user says "install the chart", "run helm install", "deploy llamacloud", "upgrade", or naturally follows a successful preinstall-check. Runs with `--wait --timeout` and intentionally **does not** pass `--atomic`, so failed pods remain in place for the `debug` skill to investigate. Read-only against the cluster except for the helm install itself.
debug
by run-llamaDiagnose problems with an installed LlamaCloud release — pods crashing, services not reachable, login broken, parses failing, OCR queue stuck, etc. Use when the user says "something's wrong", "pods are stuck", "X is failing", "the install came up but something is broken", or pastes a kubectl error. Performs a structured symptom-to-cause walk using read-only kubectl and cloud CLI commands, then matches against the LlamaCloud-specific pattern library in `patterns.md`. Produces a `debug-report.md` safe to share with LlamaIndex support.
preinstall-check
by run-llamaValidate a values.yaml and verify cluster + dependency reachability before running `helm install` for the LlamaCloud chart. Use when the user says "preinstall check", "validate my values", "check before install", "preflight", or before any first-time install or upgrade. Performs static schema validation, cluster capacity inspection, local network probes, and read-only routing checks against managed cloud services (AWS / Azure / GCP). Fully read-only by default; only one optional phase can create a short-lived Job, and only with explicit user consent.
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.