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...
mz-platform-checks
by MaterializeIncCreate/modify/debug platform check. Trigger: "platform check", "platform-checks", "upgrade check", "restart check", or writing Check class testing feature survival across restarts/upgrades. Also edits in misc/python/materialize/checks/all_checks/.
mz-parallel-workload
by MaterializeIncExtend parallel-workload stress framework: random SQL concurrently to catch panics + unexpected errors (not perf — see mz-benchmark). Trigger: "parallel workload", "parallel-workload", "action.py" re parallel workload, or testing panics/unexpected errors under concurrency. Also "add this to parallel workload" or bug that panics under concurrent DDL/DML.
mz-limits-test
by MaterializeIncAdd/modify/debug limits test. Trigger: "limits test", "Generator subclass", "many objects", "scaling test", or stress-test Materialize with many objects (tables, views, sources, indexes). Also edits in test/limits/mzcompose.py.
mz-debug-ci
by MaterializeIncInvestigate CI failures on PR via gh + bk CLI. Trigger: failing checks, Buildkite failures, CI issues — "why is CI red", "build broken", "checks failing", "what went wrong in CI", "nightly broke", "tests failing on this PR", or pasted Buildkite URL. Also PR number + why failing.
mz-dbt-release
by MaterializeIncCut a dbt-materialize PyPI release: bump the version in `__version__.py` and `setup.py`, date the `Unreleased` CHANGELOG entry, and open the release PR with a `Ship: <url>` body. Trigger: "cut a dbt release", "release dbt-materialize", "release the dbt adapter", "ship dbt-materialize vX.Y.Z", "publish dbt-materialize to PyPI", "bump dbt-materialize version", "new dbt adapter version". Use this skill even if the user just says "ship the dbt adapter" or pastes a feature PR and asks for "the next dbt release" without naming version mechanics.
mz-commit
by MaterializeIncTrigger: "commit", "prepare commit", "create PR", "push", "open pull request", or mentions committing, pre-commit checks, pull requests in Materialize. Also "ship it", "ready to merge". For code review use mz-pr-review.
mz-pr-review
by MaterializeIncLocal code review of current branch vs Materialize standards. Trigger: "review my code", "review my changes", "check my diff", "does this look ok", "what do you think of this PR", "code review", or look over changes before merging. Also PR number + wants feedback on quality, style, correctness.
mz-adapter-guide
by MaterializeIncCorrectness invariants + architecture: adapter, coordinator, pgwire, peek paths, timestamp oracle. Trigger: questions about these subsystems — "how does coordinator work", "what are read holds", "explain peek path", "how does timestamp selection work", "why does this query block". Also edits in src/adapter/, src/pgwire/, src/timestamp-oracle/.
mz-benchmark
by MaterializeIncAdd/modify/debug Materialize perf benchmark scenarios. Three frameworks: Feature Benchmark (single-op micro), Scalability Test (SQL throughput under concurrency), Parallel Benchmark (sustained latency via scenarios.py). Trigger: "benchmark", "feature benchmark", "scalability test", "parallel benchmark", "performance regression", "micro-benchmark", "TPS", "latency test", or edits in feature_benchmark/scenarios/, scalability/workload/workloads/, parallel_benchmark/scenarios.py. Note: measurement, not panic-stress (see mz-parallel-workload).
mz-profile
by MaterializeIncTrigger: "profile Materialize", "check memory usage", "analyze binary size", "debug performance", or mentions profiling, samply, heaptrack, flame graphs, memory checking, binary size, slow queries, high CPU/memory in Materialize. Also "slow" or "using too much memory" without explicit profile mention.
mz-query-tracing
by MaterializeIncDebug SQL execution time via distributed tracing (OpenTelemetry / Tempo). Trigger: "why is this query slow", "where is the time going", "this SELECT takes forever", or latency breakdown for SQL statement. Also tracing queries, span analysis, Tempo traces, trace IDs, opentelemetry_filter.
mz-run
by MaterializeIncTrigger: "run Materialize locally", "start environmentd", "check compilation", "format code", "lint", "cargo check", "cargo fmt", "cargo clippy", "bin/fmt", "bin/lint", or mentions compiling, building, running, formatting, linting, log filters, jemalloc, CockroachDB setup in Materialize. Also "how do I run this" or "it won't compile".
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.