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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clickhouse-pr-description
by AltinityGenerate PR descriptions for ClickHouse/ClickHouse that match maintainer expectations. Use when creating or updating PR descriptions.
ubrella-clickhose-review
by AltinityUse when the user asks for a multi-perspective review of a ClickHouse / C++ diff, PR, branch, commit range, or commit hash. Triggers on requests like "review this PR", "umbrella review", "ubrella review", "do a full review", "deep review of branch X", or any ClickHouse code review where multiple independent angles (security, perf, concurrency, lifetime, compat, tests, etc.) should be considered before producing a consolidated report.
fix-sync
by AltinityFix the "CH Inc sync" job in a pull request by resolving conflicts in the corresponding clickhouse-private sync PR.
audit-review
by AltinityPerform deep feature audits with transition-matrix and logical fault-injection validation. Use when reviewing complex changes, regressions, state-machine behavior, config interactions, API/protocol flows, and concurrency-sensitive logic.
codebase-consistency-reviewer
by AltinityUse when asked to review a PR, commit, commit range, branch, patch, or pasted diff for duplicated functionality, reinvented wheels, not-invented-here patterns, parallel abstractions, inconsistent naming, inconsistent settings/APIs/schemas/metrics/errors/logs, redundant config keys, non-standard implementations, or places where new code should reuse, generalize, extend, or align with existing codebase patterns and user-facing conventions. Triggers on phrases like "review for duplication", "consistency review", "is this already implemented", "does this match our conventions", "reinventing the wheel".
create-worktree
by AltinityCreate a ClickHouse git worktree with submodules hardlinked from the main repo. Use when the user wants to create a new worktree for ClickHouse development.
review
by AltinityReview a ClickHouse Pull Request for correctness, safety, performance, and compliance. Use when the user wants to review a PR or diff.
antalya-feature-design
by AltinityScaffold or review a feature design document for ClickHouse / Antalya. Use this whenever a developer wants to design or implement a new feature, add a SQL function, add a setting, add a new engine or format, or change server behavior in a non-trivial way — even if they don't explicitly ask for a "design doc". Also use when reviewing an existing design before implementation starts.
bisect
by AltinityBisect a ClickHouse regression using pre-built master binaries from CI. Use when the user wants to find the commit that introduced a bug.
codebase-consistency-reviewer
by AltinityUse when asked to review a PR, commit, commit range, branch, patch, or pasted diff for duplicated functionality, reinvented wheels, not-invented-here patterns, parallel abstractions, inconsistent naming, inconsistent settings/APIs/schemas/metrics/errors/logs, redundant config keys, non-standard implementations, or places where new code should reuse, generalize, extend, or align with existing codebase patterns and user-facing conventions. Language-agnostic — works on Go, C++, Python, TypeScript, etc. Triggers on phrases like "review for duplication", "consistency review", "is this already implemented", "does this match our conventions", "reinventing the wheel", "is there a helper for this".
test-mcp-connector
by AltinityRegister a temporary MCP connector in claude.ai and/or chatgpt.com, verify it works, then offer cleanup. Trigger when the user wants to test an MCP server URL through a real AI frontend — phrases like "test mcp", "add test connector", "smoke-test mcp server", "register connector for testing", "check mcp in claude", "check mcp in chatgpt".
umbrella-go-review
by AltinityUse when the user asks for a multi-perspective review of a Go diff, PR, branch, commit range, or commit hash. Triggers on requests like "review this PR", "umbrella review", "do a full review", "deep review of branch X", or any Go-codebase code review where multiple independent angles (security, perf, concurrency, lifetime, compat, tests, etc.) should be considered before producing a consolidated report. Adapted for altinity-mcp from the upstream ClickHouse `umbrella-clickhose-review` skill — same workflow, Go-flavored subagents (drops C++ headers + lifetime; reframes security around OAuth/secret-handling, perf around GC/goroutines/channels, compat around helm-chart/image-rolling/OAuth-config rename).
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.