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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cpp-coding
by timeplus-ioWrite or review Timeplus/Proton C++20 code covering naming conventions, Proton fences, clang-format, IProcessor patterns, and checkpointing. Make sure to use this skill for any C++ code changes, additions, or reviews in this codebase, including small fixes, new functions, refactoring, or style questions, even if the user doesn't explicitly mention coding conventions.
sql-usage
by timeplus-ioTimeplus streaming SQL covering stream types, EMIT policies, window functions, JOINs, materialized views, external streams, and UDFs. Make sure to use this skill for any SQL-related question including writing queries, debugging SQL errors, understanding streaming behavior, or designing stream processing pipelines, even if the user doesn't explicitly mention streaming SQL.
ci-diagnostics
by timeplus-ioDiagnose Proton CI failures and performance comparison results from GitHub checks and uploaded reports. Make sure to use this skill whenever CI checks are mentioned, a PR has red or failing checks, the user pastes a CI URL, or asks about test failures in the pipeline, even if they just ask 'why is CI failing'.
alloc-profile
by timeplus-ioAnalyze jemalloc or async-profiler allocation profiles in collapsed stack format. Make sure to use this skill whenever the user mentions memory profiling, heap analysis, allocation hotspots, jemalloc, async-profiler, collapsed stacks, .folded files, or asks about memory usage patterns.
build-and-verify
by timeplus-ioBuild, compile, run server/client/cluster, execute unit/stateless/SQL tests, verify results, and troubleshoot build/test failures. Make sure to use this skill whenever the user mentions building, compiling, ninja, cmake, running tests, test failures, starting the server, or pre-commit checks, even if they just say 'it doesn't build' or 'the test broke'.
review
by timeplus-ioReview a Proton pull request or diff for correctness, streaming semantics, checkpointing, compatibility, and performance. Make sure to use this skill whenever the user mentions reviewing code, checking changes, PR feedback, or wants an opinion on a diff, even if they just say 'look at this PR' or 'is this change safe'.
create-worktree
by timeplus-ioCreate a Proton git worktree with local submodule reuse. Make sure to use this skill whenever the user wants to work on a separate branch in isolation, needs a clean working copy, or mentions worktrees, even if they just say 'I want to start working on issue X in a separate directory'.
vistral
by timeplus-ioUse when building React streaming data visualizations with @timeplus/vistral — creating charts, writing VistralSpec, using VistralChart, configuring streaming data sources, or wiring up live data via ChartHandle.
timeplus-design
by timeplus-ioBuild user interfaces that match the Timeplus Console look and feel. Use this skill when generating, styling, or reviewing UI for a Timeplus product or console-aligned app — buttons, inputs, toggles, tables, cards, layouts — and when you need the exact Timeplus colors, Inter typography scale, 4px-radius flat-surface shapes, spacing scale, and component states. Provides a DESIGN.md (google-labs-code/design.md token format) plus ready-to-adapt CSS, Tailwind, and React implementations.
cisco-asa-syslog
by timeplus-ioParse, interpret, and analyze Cisco ASA (Adaptive Security Appliance) firewall syslog messages. Use this skill whenever working with Cisco ASA log files, syslog streams from ASA devices, firewall event analysis, or security investigations involving ASA-generated events. Covers the syslog protocol foundation and the ASA-specific message format with message ID categorization.
pulsebot-app-builder
by timeplus-ioBuild real-time Timeplus data processing and analysis applications in Pulsebot. Creates pure frontend HTML/JavaScript apps that connect directly to Timeplus Proton via @timeplus/proton-javascript-driver (UMD), visualize live streaming data with @timeplus/vistral (UMD), and follow the Timeplus UI style guide. No npm build or bundler required — output is a single self-contained HTML file.
searxng-web-search
by timeplus-ioSearch the web using a self-hosted SearXNG metasearch engine. Use when the user asks to search the web, find information online, look up recent news, research a topic, or needs current data from the internet. Also use when the agent needs to gather external context to answer a question. Requires a running SearXNG instance with JSON API enabled.
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.