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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shot-specifier
by leynosPer-shot production specification workflow: takes a completed scene inventory (from scene-inventory-extractor-v2) and decomposes every scene into numbered shots with full directorial direction — actor position and movement, camera mount and motion, lens, lighting setup, practical effects, timing, and clip boundaries. Generates storyboard keyframe images via nanobanana, assembles video generation prompts with model routing, and maintains an asset pipeline with consistent file naming and a generation manifest. Use when a scene inventory exists and the workflow must move from scene descriptions to individual, generation-ready clips. Also trigger when the user mentions "shot list", "shot breakdown", "storyboard", "video prompt", "model routing", "clip generation", or "per-shot direction".
seedance-2-deep-dive
by leynosDeep operating guidance for Seedance 2.0 video generation. Use when selecting Seedance 2.0 for a shot, designing multimodal references, writing Seedance-native prompts, choosing duration/aspect/quality settings, planning batch generations, troubleshooting drift or artifacts, or comparing Seedance 2.0 against Kling, Veo, Sora, DoP/Cinema, or other Higgsfield video routes. Complements shot-specifier and video-generator by turning Seedance 2.0's multimodal model behaviour into practical shot-planning and generation rules.
scene-inventory-extractor
by leynosEnd-to-end production-prep workflow: extracts comprehensive scene inventories from narrative writing, extracts continuity inventory and reset-critical state before prompt writing, generates all reference images (characters, locations under multiple angles/conditions, props), produces start/end/keyframe shot references with consistency verification, and then hands off to shot-specifier for per-shot direction, model routing, and prompt manifests. Use when analysing stories, scripts, or prose to create production-ready scene breakdowns with full visual asset pipelines. Also trigger when the user mentions "scene breakdown", "shot list", "character bible", "location bible", "continuity inventory", "reference images", "storyboarding", or any request to prepare narrative material for AI video generation. This skill expects access to an image-generation MCP and vision capabilities.
kling-3-0-deep-dive
by leynosDeep operating guidance for Kling 3.0 video generation. Use when selecting Kling 3.0 for a shot, designing multi-shot scene structure, writing Kling-native cinematic prompts, planning Elements or Motion Control references, using start/end frame anchors, handling native audio or dialogue, building product/commercial shots, choosing duration/aspect/quality settings, troubleshooting artifacts, or comparing Kling 3.0 against Seedance 2.0, Veo, Sora, DoP/Cinema, or other Higgsfield video routes. Complements shot-specifier and video-generator by turning Kling 3.0's scene-based model behaviour into practical production rules.
media-project
by leynosPackage completed visual storytelling video outputs into OpenShot editor projects with the system-installed media-project command. Use when an agent needs to run or verify a playable .osp handoff, preserve production sidecar metadata, or decide whether a project is ready for OpenShot packaging.
video-generator
by leynosExecute production video generation from shot-specifier outputs through the Higgsfield Model Context Protocol (MCP). Use when prompts, storyboard frames, media roles, model routing, Higgsfield MCP uploads, generate_video calls, status polling, retakes, resume behaviour, or final assembly order are needed. Bridges structured [TAG] prompt files to model-native plain text prompts, validates model duration/aspect constraints, decomposes key-frame shots into supported start/end image clips, tracks uploaded media and job IDs, and writes generation logs.
biome-typescript
by leynosConfigure and use Biome (biomejs) for TypeScript linting and formatting. Use when setting up Biome in a project, configuring lint rules, migrating from ESLint/Prettier, fixing lint errors, setting up CI pipelines with Biome, or configuring git hooks for code quality. Covers biome.json configuration, file inclusion/exclusion patterns, rule overrides, and integration with build tooling.
ansible-testing
by leynosLocal-first Ansible testing for roles and modules within collections. Use whenever the user wants to add, run, scaffold, or debug tests for an Ansible collection, role, or module. Triggers include: "test my Ansible role", "add Molecule tests", "run ansible-test", "set up integration tests", "scaffold a test scenario", "run sanity checks", "write unit tests for a module", "test a collection locally". Always prefer this skill over ad hoc shell suggestions when Ansible testing is the subject. The skill covers the full testing stack: Molecule + Podman for roles, ansible-test for modules and collections (sanity, unit, integration). Python 3.12+, collections layout, and Podman are assumed throughout.
nextest
by leynosUse when running Rust tests with cargo-nextest. Keywords: cargo nextest, nextest, test runner, run tests, test parallelism, test threads, flaky test, retry, slow test, timeout, test group, filterset, test partition, sharding, continuous integration (CI) testing, nextest profile, nextest config, .config/nextest.toml, cargo nextest run, cargo nextest list, test archive, JUnit, stress test, miri, test coverage, cargo-mutants, criterion, debugger, tracer
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.