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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dataset-manager
by sirius-dbUse this skill to generate benchmark datasets (TPC-H, TPC-DS, etc.). Trigger when the user needs test data at a specific scale factor for benchmarking or testing. Supports parquet and duckdb output formats.
update-docs
by sirius-dbUse this skill to update Super Sirius documentation after code changes. Trigger when the user says "update docs", "refresh documentation", "sync docs with code changes", or after merging PRs that changed the Super Sirius codebase. Inspects merged PRs since the last update and patches affected doc files.
validate
by sirius-dbUse this skill when a Sirius query returns wrong results, missing rows, extra rows, or incorrect values compared to DuckDB CPU. Pinpoints the faulty operator using per-operator row counts and data checksums. Also detects CUDA stream synchronization issues that cause garbage data.
log-analyzer
by sirius-dbUse this skill to analyze Sirius query execution logs — to find why a query is slow, diagnose validation errors (wrong results), spot excessive downgrading, identify pipeline gaps, or compare two runs of the same query. Trigger whenever the user mentions a Sirius log file, says "look at this log", asks about per-operator time, pipeline gaps, memory pressure, downgrade behavior, validation/correctness mismatches, or wants to compare two query runs. Also use as a hand-off target from race-check (for two-run comparisons), runtime-errors (for "where did the query stall before hang"), and validate (for diffing the bad run against a good run). This is the log-only counterpart to profile-analyzer (nsys) and optimization-advisor (source-level guidance).
benchmark
by sirius-dbRun TPC-H benchmarks on Super Sirius or DuckDB CPU baseline — generate data, execute queries, validate results, and compare timings. Trigger when the user mentions benchmarking, TPC-H, performance testing, query runtimes, or wants to compare Sirius vs DuckDB speed.
bisect
by sirius-dbUse this skill to find which commit introduced a bug or regression. Uses git bisect with automated build and test. Trigger when a bug appeared recently, a query started failing, performance regressed, or the user wants to compare behavior between two commits.
build-errors
by sirius-dbUse this skill when the build fails, compilation errors occur, or you see undefined references, linker errors, CUDA compilation issues, missing headers, or template instantiation failures. Analyzes errors, suggests fixes, and iteratively rebuilds until success.
config-optimizer
by sirius-dbUse this skill to find the optimal Sirius configuration for TPC-H workloads at any scale factor. Trigger when the user wants to tune performance, optimize config parameters, find the best thread count, batch size, or cache mode, or benchmark different Sirius configurations against each other. Also use when the user mentions "config tuning", "parameter sweep", or "optimal settings".
module-context
by sirius-dbAutomatically identify which dependency library modules are relevant to a task and load their API documentation into context. Use PROACTIVELY before implementing features, fixing bugs, or writing new operators — analyzes the task description and loads cudf, rmm, duckdb, cucascade module docs to improve code quality. Trigger when the user asks to implement, add, fix, or modify GPU operators, pipeline components, memory management, joins, aggregations, sorting, expressions, or data I/O.
module-discover
by sirius-dbDiscover and document a dependency library or submodule — analyzes all uses within the codebase, divides the library into logical modules, identifies which modules are used, and generates LLM-consumable API documentation for each module. Use when the user wants to understand a library dependency, map its modules, or generate API reference docs for a submodule.
optimization-advisor
by sirius-dbUse this skill to find exactly which source code to optimize for better GPU performance. Maps nsys profile hotspots to specific Sirius source files and functions, classifies bottlenecks as GPU-bound, CPU-bound, or sync-bound, and recommends actionable code changes. Trigger when the user wants to know what to optimize, where to focus coding effort, or wants source-level optimization guidance. This skill focuses on actionable source code targets — for generating performance reports and measurements, use profile-analyzer instead.
profile-analyzer
by sirius-dbUse this skill to understand why a Sirius query is slow, identify GPU bottlenecks, or detect performance regressions. Generates reports with kernel occupancy, memory bandwidth, operator attribution, and cross-run comparisons. Trigger when the user mentions profiling, nsys, GPU utilization, kernel analysis, performance reports, or wants to compare query timings across runs. This skill focuses on measurement and reporting — for mapping hotspots to source code fixes, use optimization-advisor instead.
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.