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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kanvas-crud
by bakaphpCanonical pattern for building a new domain CRUD in Kanvas (DTO + Create/Update Actions + GraphQL Mutation + schema + tests). Load when scaffolding a new entity under `src/Domains/{Domain}/{Entity}/` or its GraphQL surface in `app/GraphQL/{Domain}/Mutations/{Entity}/` and `graphql/schemas/{Domain}/`. Skip for connectors (use kanvas-connector) or when only editing existing CRUD code.
kanvas-connector
by bakaphpBuild a new external-service integration under `src/Domains/Connectors/{ConnectorName}/` — Handler + Client + DTO + Enums + Webhook job + Workflow activity + GraphQL setup mutation + `integrations` row. Load when adding/editing a connector (Shopify, Stripe, WaSender, Microsoft, OpenClaw, Hermes, etc.) or its associated `app/GraphQL/Connector/` / `graphql/schemas/Connector/` files. **NOTE**: `AgentRuntime` is a primary domain at `src/Domains/Intelligence/AgentRuntime/`, NOT a connector — see the "AgentRuntime is a primary domain" section before touching it.
kanvas-search
by bakaphpAdd `@search` to a GraphQL list query — pick `DatabaseSearchableTrait` vs `DynamicSearchableTrait`, implement `searchableAs()`/`toSearchableArray()`/`shouldBeSearchable()`, and (critical) override `search()` to scope by `apps_id` + `companies_id` so search doesn't leak across tenants. Load when adding `search: String @search` to a query, adding a searchable trait to a model, or auditing/fixing multi-tenant search scoping.
kanvas-core
by bakaphpExpert guidance for working with the @kanvas/core TypeScript SDK — the official client for the Kanvas Niche ecosystem. Use this skill whenever the user mentions @kanvas/core, KanvasCore, Kanvas SDK, or asks about integrating with a Kanvas backend. Triggers for any task involving Kanvas client setup, authentication, file uploads, commerce (cart/orders), locations, settings, or building custom Kanvas modules. Also triggers when migrating from the legacy KanvasCore class to the new modular API, or when debugging GraphQL/network errors from a Kanvas-backed app. Apply this skill even if the user just says "kanvas" in a development context — they almost certainly need SDK help.
nervous-system-working
by bakaphpHow an agent finds work assigned to it, plans the work as a checklist, executes it task by task, asks humans for approval when required, and leaves comments to communicate. Use this skill any time the agent is asked to "do" something for a user or company — the work belongs in a Plan, the steps belong in Tasks, and the conversation belongs in the Activities channel. Triggers on phrases like "find my work", "what do I have to do", "I need to plan this", "can you do X", "block until human approves", "leave a note for the user", "I'm stuck and need help".
kanvas-api-wizard
by bakaphpUse this skill whenever the user needs to write, debug, or understand GraphQL queries or mutations for the Kanvas API ecosystem. Trigger when the user asks about Kanvas entities, mutations, queries, types, enums, or inputs — even if they don't explicitly say "GraphQL". Also trigger for questions like "how do I create a X in Kanvas", "what fields does Y have", "which input do I use for Z", or any task that requires reading the Kanvas API schema before writing code. Always use this skill before writing any Kanvas GraphQL — never guess types, arguments, or enum values from memory.
kanvas-crm
by bakaphpUse when interacting with the Kanvas CRM to manage leads, pipelines, send template emails, create follow-ups, upload files, and log sales activity.
sa-kanvas-graphql
by bakaphpSafe GraphQL runtime interaction skill for Kanvas Ecosystem API and Sales Assist environments.
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.