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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google-zx-scripting
by damusixWrites and executes JavaScript-based shell scripts using Google's zx library. Use when writing shell scripts, automation, build tools, file processing, CLI tools, deployment scripts, data pipelines, or batch operations. Also covers piping, streams, parallel execution, retries, cross-platform scripting, built-in fs utilities, and minimist argument parsing.
mssql-server
by damusixWrites, optimizes, and debugs T-SQL queries. Explains SQL Server internals, troubleshoots performance issues, and guides database administration tasks including backup/restore, high availability, security, and index design. Use when the user asks about T-SQL syntax, SQL Server administration, query performance, stored procedures, indexes, locking, transactions, backup/restore, high availability, security, or any MSSQL-related topic — even without saying 'SQL Server' explicitly. Also trigger on terms like SSMS, tempdb, bcp, sqlcmd, MSSQL, sp_executesql, NOLOCK, columnstore, Hekaton, RCSI, param sniffing, or execution plan.
sql-bi-reporting
by damusixUse when writing T-SQL for business intelligence, analytics, or reporting. Includes building summary reports with GROUPING SETS, ROLLUP, and CUBE, writing time-series queries with date bucketing, creating pivot/unpivot transformations, generating tally/numbers tables for gap-filling, building running totals and moving averages with window functions, writing year-over-year comparisons, designing materialized views for dashboards, or producing CSV/JSON exports from SQL Server.
sql-server-performance
by damusixDiagnoses and optimizes SQL Server database performance. Use when diagnosing slow T-SQL queries, tuning indexes, reading execution plans, fixing parameter sniffing, optimizing batch operations, reducing transaction log bloat, troubleshooting locking and blocking, configuring tempdb, or when a query that used to be fast is now slow. Also use when writing high-throughput INSERT/UPDATE/DELETE operations, implementing minimal logging, designing covering indexes, or analyzing wait statistics.
mssql-writing-guidelines
by damusixUse when writing or reviewing MSSQL/T-SQL, creating stored procedures, designing table schemas, writing views, building migrations, defining custom types, or architecting a SQL Server application database. Also use when writing RAISERROR patterns, CHECK constraints with scalar functions, base/subtype table hierarchies, composite key designs, role-scoped views with row-level security, or idempotent DDL scripts. If you are touching MSSQL for an application database, use this skill. Not for PostgreSQL, MySQL, Oracle, or SQLite — patterns are SQL Server-specific.
ast-grep
by damusixAST-based code search, lint, and rewrite using ast-grep. Use when finding code patterns structurally (not textually), writing lint rules, building codemods, or migrating API usage across a codebase. Prefer over regex grep when the match target is a syntactic construct (function call, import, class field, assignment).
postgres
by damusixComprehensive PostgreSQL reference for developers and DBAs covering versions 14–18. Use whenever the user asks about PostgreSQL syntax, DDL/DML/DQL, joins, LATERAL, CTEs, window functions, GROUPING SETS, DISTINCT ON, RETURNING, ON CONFLICT, PL/pgSQL, functions, procedures, triggers, views, materialized views, indexes (B-tree/GIN/GiST/BRIN/Hash/Bloom), MVCC, VACUUM, autovacuum, WAL, TOAST, partitioning, replication (streaming/logical), backup, PITR, HA (Patroni/repmgr), pgBouncer, EXPLAIN ANALYZE, RLS, roles, extensions (pgvector, PostGIS, TimescaleDB, Citus, pg_trgm, pg_cron), JSON/JSONB, full-text search, UUID, timestamptz, COPY, system catalogs, collations, large objects, cursors, GUC, or any Postgres administration, performance, security, replication, backup, or recovery topic.
postgres-writing-guidelines
by damusixUse when writing or reviewing PostgreSQL/PL-pgSQL, designing table schemas, writing functions and procedures, building migrations, defining domains, or architecting a Postgres application database. Also use when writing RAISE EXCEPTION patterns, BEFORE/AFTER triggers for cross-table constraints, base/subtype hierarchies, composite key designs, row-level security policies, or idempotent DDL scripts. If you are touching Postgres for an application database, use this skill. PostgreSQL-specific — examples will not run on other engines.
arm-bandits-expert
by damusixImplements, evaluates, and deploys multi-armed bandit algorithms — including Thompson Sampling, UCB, epsilon-greedy, LinUCB, EXP3, and contextual bandits. Covers algorithm selection, experiment harnesses, offline evaluation (IPS, Doubly Robust), infrastructure patterns, and correctness verification. Use when the user asks about multi-armed bandits, exploration-exploitation tradeoffs, adaptive experiments, A/B testing alternatives, online optimization, bandit-based recommendation or personalization systems, or contextual bandits.
atomic-verify
by damusixEvidence-before-claim gate. Auto-triggers when Claude is about to claim "done", "fixed", "passing", "complete", "ready to merge", "looks good", "should work", "should pass", "green", or any synonym. Iron rule: no completion claim without a fresh verification command run in this turn. Explicit invocation: /atomic-verify.
atomic-review
by damusixCompressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", or invokes /atomic-review. Auto-triggers when reviewing pull requests.
atomic-documentation
by damusixDiff-driven documentation surface classifier. Given a diff (staged, branch, or range), reads the project's indexed ## Documentation surfaces table from CLAUDE instructions, matches the diff against it, and emits a structured list of proposed edits. Two modes: maintenance (commit flow — stale/incomplete only, never suggests new pages) and authoring (/documentation explicit — full discovery, gap detection, content generation). Auto-fires on "doc this change", "what surfaces does this touch", "doc impact for this diff", "what needs documenting". Also invoked by /documentation (authoring mode) and by ship verbs (maintenance mode, between stage and signals). Boundary: for raw prose drafting (README intro, guide narrative), atomic-prose owns. This skill owns diff-driven surface impact and content generation for stale/incomplete docs.
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.