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...
keep-codex-fast
by vibeforge1111Use when Codex feels slow or bloated, when local sessions/logs/worktrees/config have grown over time, or when a user wants safe maintenance for Codex Desktop/CLI state. Provides a read-only report by default, backs up before applying changes, archives instead of deleting, normalizes Windows extended paths, prunes dead config projects, rotates large logs, and moves stale worktrees.
codex-visual-builder-guild
by vibeforge1111Use when Codex should improve visual UI/UX through a screenshot-driven design loop with imagegen assets, vision review, responsive checks, interaction states, accessibility, A/B variants, design tokens, art bible extraction, and specialist-style delegation. Trigger when the user asks for the Codex Visual Builder Guild, visual builder loop, imagegen plus vision workflow, designer guild, UI polish, README/X launch visuals, game UI polish, SaaS dashboard polish, or to make an app look production-ready through screenshots.
startup-advisor
by vibeforge1111Expert startup advisor with deep knowledge of Y Combinator principles, successful unicorn strategies, and building high-impact billion-dollar companies
vibeship-optimizer
by vibeforge1111Safe, rollback-friendly optimization workflow for any codebase: capture before/after snapshots, compare performance/size/health probes, and maintain a multi-day verification log. Use in OpenClaw when a user wants to optimize a project without breaking it, wants commit-per-change rollbacks, or wants a living VIBESHIP_OPTIMIZER.md validation document.
agent-landscape-analysis
by vibeforge1111Analyze OpenClaw, Claude Cowork, Hermes Agent, and adjacent agent products to learn what to borrow, what to directly copy as patterns, and what Spark Intelligence should keep uniquely its own. Use when comparing architectures, onboarding, adapters, reliability harnesses, migration paths, or competitive product shape.
maintainable-engineering
by vibeforge1111Keep Spark Intelligence code, architecture, security, and documentation highly maintainable, scalable enough, and clean without adding unnecessary complexity. Use when reviewing code, planning architecture, writing docs, simplifying modules, improving quality, or enforcing long-term engineering standards.
reliable-job-harnesses
by vibeforge1111Build and review cron jobs, schedulers, retries, smoke tests, and operational harnesses for Spark Intelligence in a lightweight and highly reliable way. Use when designing job systems, background work, health checks, retry logic, cron behavior, migration jobs, or smoke-test coverage for scheduled work.
security-auditor
by vibeforge1111Audit local-agent systems, specs, and code for security weaknesses and hardening priorities. Use when reviewing Spark Intelligence features, security-sensitive diffs, identity or pairing flows, host-execution boundaries, webhook adapters, auth and secret handling, or when producing a security grade and remediation list that others can reuse later.
security-systems
by vibeforge1111Design secure, lightweight local-agent systems for Spark Intelligence and adjacent agent products. Use when defining identity and pairing rules, provider or channel auth, dangerous command approvals, tool and host boundaries, webhook trust checks, local secret handling, or security guardrails for new features.
spark-ecosystem-product
by vibeforge1111Make Spark Intelligence product decisions with deep awareness of Spark Researcher, Spark Swarm, domain chips, specialization paths, autoloop flywheels, and the wider Spark ecosystem. Use when defining product scope, modular architecture, ecosystem integration, persistent agent behavior, or self-evolving collective-intelligence systems.
evolution-engine
by vibeforge1111Autonomous learning and verification system. Triggers on: - Session start (runs verification sweep) - User corrections ("no", "wrong", "I told you", "we don't do that") - Task completion (session scoring) - Discoveries during work (hypothesis verification) - User explicit ("remember this", "add this as a rule")
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.