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...
ui-native-package
by reliant-labsThe `@<scope>/ui-native` workspace package — what ships, the web/native gap, ownership rules, and when to outgrow it for Tamagui or Unistyles.
kcl-schemas-to-module
by reliant-labsMigrate a forge project's in-tree `deploy/kcl/schema.k` / `base.k` / `render.k` / `lib/*.k` to the upstream `forge` KCL module. ~3000 lines of duplicated schema deletes; projects import typed entities (Service / Operator / Frontend / CronJob with polymorphic deploy) instead of constructing the legacy `Application` struct.
v0-x-to-pack-starter-split
by reliant-labsMigrate stripe / twilio / clerk-webhook from packs to starters. forge versions before 1.6 shipped these as installable packs; 1.6+ ships them as one-time-copy starters that the user owns.
v0-1-to-v0-2
by reliant-labsMigrate a forge project from v0.1's two-phase Bootstrap+Setup+ApplyDeps DI shape to v0.2's codegen-based wire_gen.go single-phase DI. The change unifies dep construction so validateDeps gates the COMPLETE dep set at New(), eliminating per-RPC nil-check boilerplate. Use when bumping forge_version from v0.1 to v0.2.
v0-x-to-authz-lib
by reliant-labsMigrate per-service authorizer_gen.go from inline matching logic to a thin shim over forge/pkg/authz. Same public API (NewGeneratedAuthorizer / Can / CanAccess); decision logic now lives in one tested library.
v0-x-to-checksum-history
by reliant-labsMigrate `.forge/checksums.json` from the legacy flat shape (path -> hex string) to the structured shape (path -> {hash, history[]}). The new shape lets `forge upgrade` distinguish stale codegen from genuine user edits, eliminating false-positive "user-modified (skipped)" reports on real template upgrades.
v0-x-to-contractkit
by reliant-labsMigrate generated mock/middleware/tracing/metrics shape from inline-everything to contractkit shim + library. Forge versions before 1.5 used the old shape; 1.5+ uses contractkit.
v0-x-to-crud-lib
by reliant-labsMigrate handlers_crud_gen.go from inline lifecycle code to thin per-RPC shims that delegate to forge/pkg/crud.
v0-x-to-env-config
by reliant-labsMigrate from hand-curated env-var groups in KCL to forge.yaml environments[].config (per-env config) + sensitive-field projection. forge versions before 1.6 emitted env-var soup; 1.6+ projects per-env config to ConfigMap/Secret/value automatically.
v0-x-to-observe-libs
by reliant-labsMigrate from per-internal-package middleware/tracing/metrics codegen to forge/pkg/observe Connect interceptors + opt-in helpers. Mock stays codegen.
v0-x-to-strict-contract-names
by reliant-labsInternal-package contract.go files must declare `type Service interface`, `type Deps struct`, and `func New(Deps) (Service, error)` (or the legacy single-result `func New(Deps) Service`). The convention is now lint-enforced; non-canonical names previously produced silently-broken bootstrap codegen.
v0-x-to-tdd-rpccases
by reliant-labsMigrate handlers_crud_gen_test.go from inline per-RPC test boilerplate to thin shims that delegate to forge/pkg/tdd.RunRPCCases.
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.