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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mz-test
by DAlperinGeneral guide for running tests and choosing the right test framework in Materialize. Trigger when the user wants to "run tests", "run testdrive", "run sqllogictest", "run mzcompose", "run cargo test", "run pgtest", "rewrite test results", "add a test", "reproduce a bug", "write a regression test", or mentions testing, testdrive, sqllogictest, mzcompose, pgtest, cargo test, nextest, flaky tests, or test failures. Use this skill even if the user just says "test this" or "how do I verify this works" without naming a specific framework. For deep guidance on specific frameworks, see the dedicated skills: mz-platform-checks (upgrade/restart survival), mz-parallel-workload (concurrent stress testing), and mz-limits-test (scaling to many objects).
mz-pr-review
by DAlperinPerform a local code review of the current branch's changes against Materialize project standards. Trigger when the user says "review my code", "review my changes", "check my diff", "does this look ok", "what do you think of this PR", "code review", or asks you to look over changes before merging. Also trigger when the user passes a PR number and wants feedback on quality, style, or correctness.
mz-platform-checks
by DAlperinThis skill should be used when the user wants to create, modify, or debug a platform check. Trigger when the user mentions "platform check", "platform-checks", "upgrade check", "restart check", or wants to write a Check class that tests feature survival across restarts/upgrades. Also trigger when the user edits files in misc/python/materialize/checks/all_checks/.
mz-parallel-workload
by DAlperinExtend the parallel-workload stress-testing framework, which runs random SQL actions concurrently to catch panics and unexpected errors (not performance measurement — see mz-benchmark for that). Trigger when the user mentions "parallel workload", "parallel-workload", "action.py" in the context of parallel workload, or wants to test for panics or unexpected query errors under concurrency. Use this skill even if the user just says "add this to parallel workload" or references a bug that panics under concurrent DDL/DML.
mz-limits-test
by DAlperinThis skill should be used when the user wants to add, modify, or debug a limits test. Trigger when the user mentions "limits test", "Generator subclass", "many objects", "scaling test", or wants to stress-test Materialize with large numbers of objects (tables, views, sources, indexes, etc.). Also trigger when the user edits test/limits/mzcompose.py.
mz-debug-ci
by DAlperinInvestigate CI failures on a PR using gh and bk CLI tools. Trigger when the user asks about failing checks, Buildkite failures, or CI issues — including casual phrases like "why is CI red", "build broken", "checks failing", "what went wrong in CI", "nightly broke", "tests failing on this PR", or pastes a Buildkite URL. Also trigger when the user mentions a specific PR number and wants to understand why it's failing.
mz-commit
by DAlperinThis skill should be used when the user wants to "commit", "prepare a commit", "create a PR", "push", "open a pull request", or mentions committing, pre-commit checks, or pull requests in the Materialize repository. Use this skill even if the user just says "ship it" or "ready to merge" without being specific. Note: for reviewing code, use mz-pr-review instead.
mz-benchmark
by DAlperinAdd, modify, or debug benchmark scenarios for measuring Materialize performance. Covers three frameworks: Feature Benchmark (single-operation micro-benchmarks), Scalability Test (SQL throughput under concurrency), and Parallel Benchmark (sustained latency over time via scenarios.py). Trigger on "benchmark", "feature benchmark", "scalability test", "parallel benchmark", "performance regression", "micro-benchmark", "TPS", "latency test", or when editing files in feature_benchmark/scenarios/, scalability/workload/workloads/, or parallel_benchmark/scenarios.py. Note: this is about benchmark measurement frameworks, not the parallel-workload stress-testing framework (which tests for panics under concurrency, not performance).
mz-adapter-guide
by DAlperinCorrectness invariants and architectural guidance for the adapter layer, coordinator, pgwire, peek paths, and timestamp oracle. Trigger when the user works on or asks questions about these subsystems — including "how does the coordinator work", "what are read holds", "explain the peek path", "how does timestamp selection work", "why does this query block". Also trigger when editing files in src/adapter/, src/pgwire/, or src/timestamp-oracle/.
mz-query-tracing
by DAlperinDebug where time is spent during SQL execution using distributed tracing (OpenTelemetry / Tempo). Trigger when the user asks "why is this query slow", "where is the time going", "this SELECT takes forever", or wants a latency breakdown for any SQL statement. Also trigger on mentions of tracing queries, span analysis, Tempo traces, trace IDs, or opentelemetry_filter.
mz-run
by DAlperinThis skill should be used when the user wants to "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, or CockroachDB setup in the Materialize repository. Use this skill even if the user just says "how do I run this" or "it won't compile" without being specific.
mz-profile
by DAlperinThis skill should be used when the user wants to "profile Materialize", "check memory usage", "analyze binary size", "debug performance", or mentions profiling, samply, heaptrack, flame graphs, memory checking, binary size analysis, slow queries, or high CPU/memory usage in the Materialize repository. Use this skill even if the user just says something is "slow" or "using too much memory" without explicitly mentioning profiling.
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.