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...
convert-calendar
by rhiza-researchConvert a Rhiza Envelope Zarr's time axis to a target CF calendar by wrapping xarray's Dataset.convert_calendar. Use to align two datasets onto a common calendar before comparison — e.g. converting a model-calendar forecast (noleap/360_day) to the standard calendar of observations. Converting to a standard calendar yields a datetime64 axis; converting to a model calendar yields cftime. Dates not representable in the target calendar are dropped.
cmip6-fetch
by rhiza-researchFetch a CMIP6 climate-model projection (e.g. temperature, precipitation) for a date range and region from the public, credential-free Pangeo Google Cloud catalog, and write a weather-skills envelope Zarr. Use when a task needs climate-projection grids (historical or future scenario) for downstream clipping, aggregation, comparison, or plotting.
provenance
by rhiza-researchInspect the weather_skills_history provenance chain stamped on a weather-skills artifact (an envelope Zarr or a plot PNG) and render it as a human-readable lineage, the raw JSON chain, or a runnable reproduction script. Use when you need to answer "how did this file come to exist, and how do I regenerate it?" — especially for a PNG, whose chain lives in binary tEXt chunks an editor can't open.
oisst-fetch
by rhiza-researchFetch NOAA OISST v2.1 daily sea-surface temperature for a date range and region from NOAA PSL's public OPeNDAP server, and write a Rhiza Envelope Zarr. Use when a task needs credential-free gridded SST observations, e.g. for ocean analysis or comparison against forecasts/reanalysis.
plot-mediogram
by rhiza-researchRender an ECMWF-style mediogram PNG comparing a forecast ensemble against an m-climate (historical) ensemble at a single lat/lon. Two-layer boxplots per time step show an extremes box underneath (p0–p100 whiskers, p10–p90 box, p50 median) with a wider p25–p75 IQR box overlaid on top, whose visible black caps mark the IQR edges.
plot-timeseries
by rhiza-researchRender a single PNG with one 1D trace per input Zarr overlaid on a shared time axis. Use when you want to compare a variable across multiple weather-skills envelope Zarrs as line traces. Inputs whose variable still has non-time dims after selection must list those dims via repeated --reduce flags; no silent averaging.
plot
by rhiza-researchRender a 2D heatmap or 1D time series PNG from any gridded or station weather-skills envelope Zarr. Use when you need to visualize a single dataset as a map or as a time/step profile.
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.