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...
project-readiness
by c9r-ioVerify a project is ready for release by checking local Docker Compose startup, Kubernetes manifest sanity, GitHub Actions status via gh, and test/coverage commands. Use when the user asks to confirm readiness, pre-release checks, a release checklist, or to diagnose why CI/build/deploy is failing right before release.
design-system-guidance
by c9r-ioConsult and apply the project design system when discussing UI design, visual language, design tokens, component variants, accessibility, theming, or styling conventions for the portal frontend. Use when a user asks about design system changes or implementing UI components/styles.
e2e-testing
by c9r-ioRun and author Playwright E2E tests for the project (frontend-only or full-stack). Use when the user asks to add E2E tests, run Playwright tests, validate critical user journeys, or stabilize flaky E2E tests.
fr-governance
by c9r-ioGovern feature request (FR) documents through their full lifecycle — from planning to implementation to closure. Use when the user asks to govern/治理 a feature request, close an FR, check FR status, or says "治理FR", "/fr-governance". Scans docs/feature_request/ for open FR docs, plans implementation, executes governance, and self-checks closure.
grpc-regression
by c9r-ioRun grpcurl checks from inside the Docker Compose network (useful when host-to-container ports are blocked).
guide-alignment
by c9r-ioCompile-driven user guide alignment. Builds the project, walks the full CLI --help tree, compares against docs/guide/ (EN+ZH), auto-fixes drift, and outputs an alignment report. Use when docs may have drifted from CLI implementation.
integration-authoring
by c9r-ioCreate or update integration manifest packages in the companion repo (orchestrator-integrations). Use when a user asks to add a new integration (Slack, GitHub, Discord, Jira, etc.), create a webhook trigger for an external service, or extend the integrations library. Routes all integration work to the separate repo while reusing the main project's SDLC infrastructure.
ops
by c9r-ioRun tests, check logs, and troubleshoot services in Docker Compose and Kubernetes environments. Use when debugging local dev env issues, inspecting container/pod logs, restarting services, checking health, or triaging deployment rollouts.
orchestrator-guide
by c9r-ioGuide for working with the Agent Orchestrator — a CLI tool for AI-native SDLC automation. Use when writing or editing YAML manifests (Workspace, Agent, Workflow, StepTemplate, ExecutionProfile, SecretStore, EnvStore, Trigger, CRD), running orchestrator CLI commands, designing workflow step pipelines, writing CEL prehook/finalize expressions, configuring triggers (cron/event-driven task creation), or configuring self-bootstrap workflows. Triggers: any mention of orchestrator config, YAML manifests with "orchestrator.dev/v2", workflow steps, prehooks, finalize rules, task create/start/pause, orchestrator run, step filtering, direct assembly, agent capabilities, StepTemplate prompts, execution profiles, sandbox configuration, secret management, or trigger/cron scheduling.
orchestrator-test-monitor
by c9r-ioMonitor and evaluate orchestrator test execution plans end-to-end. Use when the user wants to run a test execution plan from docs/showcases/, observe the orchestrator's full-pipeline processing, and get a final assessment. Triggers on: "run test plan", "execute plan", "monitor orchestrator", "test the orchestrator", "run execution plan", "run showcase", or any request to observe/evaluate orchestrator behavior on a showcase plan. This skill is OBSERVE-ONLY — never intervene in the orchestrator's execution.
performance-testing
by c9r-ioRun lightweight performance/load tests (QPS/latency) against project HTTP endpoints using hey, track regressions, and suggest optimizations. Use when the user asks to benchmark, load test, or measure performance after changes.
project-bootstrap
by c9r-ioInitialize a brand-new project from scratch with a default full-stack scaffold (Rust core + React Router 7 portal + Docker + Kubernetes). Use when a developer asks to bootstrap an empty repo or start a new project and no specific tech stack is requested.
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.