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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ring-running-dev-cycle-frontend
by LerianStudioRunning the frontend (React/Next.js/TS) dev cycle from a plan.md (ring:writing-plans format; legacy tasks.md only for in-flight cycles) or backend handoff: drives frontend agents through Gate 0 TDD plus accessibility/visual/E2E/perf checks, Gate 7 parallel review, and Gate 8 user validation, with rolling-wave phase boundaries. Use when starting or resuming a gated frontend dev cycle. Skip for backend (use ring:running-dev-cycle) or docs-only work.
ring-running-dev-cycle
by LerianStudioRunning the backend dev cycle: implements every task in a rolling-wave plan.md (ring:writing-plans format) for a Go/TS service, driving specialist agents through Gate 0 implementation/TDD, Gate 8 parallel review, and Gate 9 validation per epic, elaborating later phases at each phase boundary. Use when starting or resuming a gated backend dev cycle with a plan.md (legacy tasks.md only for cycles already in flight; new cycles need the canonical plan format). Skip for frontend (use ring:running-dev-cycle-frontend) or docs-only work.
ring-auditing-production-readiness
by LerianStudioAuditing a service's production readiness against Ring engineering standards across base dimensions plus a conditional multi-tenant dimension, then emitting a scored report and an HTML dashboard. Use before production deploy, periodic review, onboarding, or a major release. Skip for prototypes, libraries, or single-dimension checks.
ring-committing-changes
by LerianStudioCommitting working-tree changes as atomic conventional commits: analyzes the diff, groups it into coherent commits, confirms the plan, then creates GPG-signed commits carrying the mandatory X-Lerian-Ref trailer and offers to push. Use when the user says 'commit' or has changes ready to record. Skip when the tree is clean, work is still in progress, or the user wants raw git commands without grouping.
ring-creating-handoffs
by LerianStudioCreating a handoff document that captures session state (completed work, decisions, open items, next steps) and delivering it via Plan Mode so the user gets the native 'clear context and continue implementing' resume option. Use when ending a session, when context grows large, or the user says 'handoff', 'save session', or 'context transfer'. Skip when context is minimal or work is fully complete with no resume planned.
ring-creating-worktrees
by LerianStudioCreating an isolated git worktree for parallel branch work: selects the directory by priority order, verifies/adds .gitignore safety, auto-installs the detected toolchain's dependencies, runs a baseline test, and reports readiness. Use before a feature that needs isolation from the main workspace or before executing an implementation plan. Skip for a quick fix on the current branch or when already in the feature's worktree.
ring-delegating-to-gandalf
by LerianStudioDelegating tasks to Gandalf, a Lerian AI teammate reachable over Tailscale, and returning its response. Use when you need to publish an HTML/markdown report to Alfarrabio and get a URL, post a Slack notification, or ask Gandalf for business/product context via a full agent session. Tailscale-network only, no auth token. Skip when off the Tailscale network or the task can be done locally.
ring-exploring-codebases
by LerianStudioExploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before planning changes, or to orient on an unfamiliar codebase. Skip for a single signature lookup, a file-exists check, or reading an error from a known file.
ring-fixing-lint
by LerianStudioFixing lint to a clean state: runs the linter, groups reported issues into independent streams, and dispatches one parallel fixer agent per stream (ring:backend-go for Go, ring:general-purpose otherwise), iterating until clean. Use when a codebase has lint errors across multiple files. Skip for a single error (fix directly), already-passing lint, or view-only requests; security lints are reported, not auto-fixed.
ring-generating-release-guides
by LerianStudioGenerating an internal Operations-facing update/migration guide from the git diff between two refs, documenting per-change client impact, deploy ordering, monitoring, and rollback notes in English, pt-br, or both. Use when preparing a version release or recording what changed for the Ops team. Runs read-only by default and previews before writing. Skip with no git repo or a trivial single-file change.
ring-reviewing-code
by LerianStudioReviewing code by dispatching the default reviewer subagents in parallel (plus conditional specialists for lib-observability, lib-systemplane, or lib-streaming when the diff triggers them), then aggregating findings by severity into a report. Use as Gate 8 of ring:running-dev-cycle at epic cadence over the cumulative diff, or before merging. Report-only. Skip for a single-command Go pre-merge verdict (use ring:verifying-code).
ring-test-driven-development
by LerianStudioEnforcing the RED-GREEN-REFACTOR loop: write one failing test and watch it fail, write minimal code to pass, then refactor green. Use when starting implementation of a new feature or bugfix, or writing any new production code. Requires pasted failure output as proof of RED; code written before its test must be deleted, not stashed. Skip for exploratory spikes or when only modifying existing tests.
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.