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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git-helper
by joshuadavidthomasProvides git workflow assistance, branch management, and commit message optimization
test-skill
by joshuadavidthomasA test skill to verify all plugin tools work correctly - use_skill, read_skill_file, run_skill_script, find_skills
djls-domain-conventions
by joshuadavidthomasUse when editing Django template parser tags, validation errors, diagnostics, environment scanning, inspector data, Python environment inventory, or DJLS domain model code. Handles project-specific semantic and parser conventions.
djls-ruff-ast
by joshuadavidthomasUse when editing djls-extraction or Rust code that consumes Ruff Python AST APIs, including parse_module, parameters, boxed expressions, f-strings, or exception handlers. Handles known Ruff AST shape gotchas in django-language-server.
djls-salsa
by joshuadavidthomasUse when editing Salsa inputs, tracked functions, database traits, setters, SemanticDb, crate::Db, invalidation behavior, or incremental computation in django-language-server. Handles project-specific Salsa conventions and required impl updates.
djls-testing
by joshuadavidthomasUse when running tests, fixing failing tests, updating snapshots, syncing corpus data, validating before push, or working with Django matrix testing in django-language-server. Handles cargo, just, nox, insta, corpus, clippy, fmt, and lint commands.
djls-workspace-conventions
by joshuadavidthomasUse when adding or editing crates, Cargo.toml files, workspace dependencies, crate manifests, lints, versions, or project structure in django-language-server. Handles Rust workspace layout, dependency grouping, and new crate setup.
djls-changelog
by joshuadavidthomasUse when adding release notes, changelog entries, user-facing changes, internal changelog notes, or preparing a PR/release for django-language-server. Handles Keep a Changelog formatting and entry style.
rust
by joshuadavidthomasMental-model reset for Rust. Use when writing or reviewing Rust code to shift from "it compiles" to "thinks in Rust." Triggers on Rust code review, "is this idiomatic", borrow-checker errors, API design, domain modeling, ownership, lifetimes, errors, traits, async/Tokio, unsafe, serde, FFI, tests, performance, Cargo structure, .rs files, Cargo.toml, rustc diagnostics, clippy findings, Result/Option, thiserror vs anyhow, newtype, typestate, enum vs trait, dyn Trait, Send/Sync, Pin, Miri, PyO3, napi-rs, cxx, UniFFI, wasm-bindgen, serde attributes, or feature unification.
diataxis
by joshuadavidthomasStructure, classify, and write documentation using the Diátaxis framework. Use when writing docs, README files, guides, tutorials, how-to guides, API references, or organizing documentation architecture. Also use when asked to improve documentation, restructure docs, decide what type of doc to write, or classify existing content. Covers tutorials, how-to guides, reference, and explanation.
salsa
by joshuadavidthomasMental-model reset for Salsa, the incremental computation framework for Rust. Use when building or reviewing Salsa databases, tracked functions, input/ tracked/interned structs, query pipelines, accumulators, cancellation, durability, LSP integration, memory management, cycles, or production Salsa architecture. Triggers on #[salsa::db], #[salsa::input], #[salsa::tracked], #[salsa::interned], #[salsa::accumulator], salsa::Storage, memoization, revisions, backdating, red-green algorithm, WillExecute, DidValidateMemoizedValue, Cancelled, returns(ref), no_eq, lru, cycle_fn, cycle_result, durability, or salsa::Event.
skill-authoring
by joshuadavidthomasUse when authoring, creating, refining, or troubleshooting agent skills — scaffold SKILL.md and frontmatter, write and optimize the trigger description, structure the body with progressive disclosure, validate structure, and test or debug activation. Also when building a new skill from scratch, when a skill won't trigger, loads incorrectly, or the agent ignores it entirely. Use when a skill misbehaved in the current session and needs adjustment based on learnings.
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.