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...
blog-planner
by electric-sqlInteractive blog post authoring. Produces a draft blog post file with structured outline, inline guidance comments, and meta briefs that the author proses up in place. Supports pyramid principle, best sales deck, and release post formats.
designing-entities
by electric-sqlUse when an app developer wants to build an entity (a.k.a. an agent) for their Electric Agents app — designing a single entity type, picking a coordination pattern when needed (single-agent, manager-worker, pipeline, map-reduce, dispatcher, blackboard, reactive-observer), defining state, handler, schemas, and implementing it in one entity file. Applies to any use of `registry.define(...)` / `defineEntity(...)` in a `@electric-ax/agents-runtime` app.
electric-deployment
by electric-sqlDeploy Electric via Docker, Docker Compose, or Electric Cloud. Covers DATABASE_URL (direct connection, not pooler), ELECTRIC_SECRET (required since v1.x), ELECTRIC_INSECURE for dev, wal_level=logical, max_replication_slots, ELECTRIC_STORAGE_DIR persistence, ELECTRIC_POOLED_DATABASE_URL for pooled queries, IPv6 with ELECTRIC_DATABASE_USE_IPV6, Kubernetes readiness probes (200 vs 202), replication slot cleanup, and Postgres v14+ requirements. Load when deploying Electric or configuring Postgres for logical replication.
entity-stream-queries
by electric-sqlQuerying electric agent runtime entity streams and manifest state with @durable-streams/state/db queryOnce and useLiveQuery. Use when reading built-in entity collections like manifests, wakes, child_status, inbox, runs, or shared state from runtime code, tests, examples, or CLI code. Prefer direct typed queries over one-off read helpers.
electric-debugging
by electric-sqlTroubleshoot Electric sync issues. Covers fast-loop detection from CDN/proxy cache key misconfiguration, stale cache diagnosis (StaleCacheError), MissingHeadersError from CORS misconfiguration, 409 shape expired handling, SSE proxy buffering (nginx proxy_buffering off, Caddy flush_interval -1), HTTP/1.1 6-connection limit in local dev (Caddy HTTP/2 proxy), WAL growth from replication slots (max_slot_wal_keep_size), Vercel CDN cache issues, and onError/backoff behavior. Load when shapes are not receiving updates, sync is slow, or errors appear in the console.
electric-shapes
by electric-sqlConfigure ShapeStream and Shape to sync a Postgres table to the client. Covers ShapeStreamOptions (url, table, where, columns, replica, offset, handle), custom type parsers (timestamptz, jsonb, int8), column mappers (snakeCamelMapper, createColumnMapper), onError retry semantics, backoff options, log modes (full, changes_only), requestSnapshot, fetchSnapshot, subscribe/unsubscribe, and Shape materialized view. Load when setting up sync, configuring shapes, parsing types, or handling sync errors.
electric-schema-shapes
by electric-sqlDesign Postgres schema and Electric shape definitions together for a new feature. Covers single-table shape constraint, cross-table joins using multiple shapes, WHERE clause design for tenant isolation, column selection for bandwidth optimization, replica mode choice (default vs full for old_value), enum casting in WHERE clauses, and txid handshake setup with pg_current_xact_id() for optimistic writes. Load when designing database tables for use with Electric shapes.
electric-proxy-auth
by electric-sqlSet up a server-side proxy to forward Electric shape requests securely. Covers ELECTRIC_PROTOCOL_QUERY_PARAMS forwarding, server-side shape definition (table, where, params), content-encoding/content-length header cleanup, CORS configuration for electric-offset/electric-handle/ electric-schema/electric-cursor headers, auth token injection, Bun fetch concurrency cap (BUN_CONFIG_MAX_HTTP_REQUESTS default 256), ELECTRIC_SECRET/SOURCE_SECRET server-side only, tenant isolation via WHERE positional params, onError 401 token refresh, and subset security (AND semantics). Load when creating proxy routes, adding auth, or configuring CORS for Electric.
electric-postgres-security
by electric-sqlPre-deploy security checklist for Postgres with Electric. Checks REPLICATION role, SELECT grants, CREATE on database, table ownership, REPLICA IDENTITY FULL on all synced tables, publication management (auto vs manual with ELECTRIC_MANUAL_TABLE_PUBLISHING), connection pooler exclusion for DATABASE_URL (use direct connection), and ELECTRIC_POOLED_DATABASE_URL for pooled queries. Load before deploying Electric to production or when diagnosing Postgres permission errors.
electric-orm
by electric-sqlUse Electric with Drizzle ORM or Prisma for the write path. Covers getting pg_current_xact_id() from ORM transactions using Drizzle tx.execute(sql) and Prisma $queryRaw, running migrations that preserve REPLICA IDENTITY FULL, and schema management patterns compatible with Electric shapes. Load when using Drizzle or Prisma alongside Electric for writes.
electric-new-feature
by electric-sqlEnd-to-end guide for adding a new synced feature with Electric and TanStack DB. Covers the full journey: design Postgres schema, set REPLICA IDENTITY FULL, define shape, create proxy route, set up TanStack DB collection with electricCollectionOptions, implement optimistic mutations with txid handshake (pg_current_xact_id, awaitTxId), and build live queries with useLiveQuery. Also covers migration from old ElectricSQL (electrify/db pattern does not exist), current API patterns (table as query param not path, handle not shape_id). Load when building a new feature from scratch.
electric-yjs
by electric-sqlSet up ElectricProvider for real-time collaborative editing with Yjs via Electric shapes. Covers ElectricProvider configuration, document updates shape with BYTEA parser (parseToDecoder), awareness shape at offset='now', LocalStorageResumeStateProvider for reconnection with stableStateVector diff, debounceMs for batching writes, sendUrl PUT endpoint, required Postgres schema (ydoc_update and ydoc_awareness tables), CORS header exposure, and sendErrorRetryHandler. Load when implementing collaborative editing with Yjs and Electric.
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.