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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ncmctl-dev
by chaunsinNetEase Cloud Music CLI tool (ncmctl) development guide. Use this skill when working with the netease-cloud-music codebase, including ncmctl commands, API integration, crypto, NCM file decryption, daily tasks, music download, cloud upload, or any Go code in this repository. Trigger on mentions of ncmctl, 网易云音乐 CLI, NetEase Cloud Music API, weapi/eapi encryption, .ncm file format, cloud music daily tasks (sign/partner/scrobble), or when modifying, debugging, or extending any part of this project.
ncmctl
by chaunsinncmctl CLI reference and usage guide for NetEase Cloud Music. Use this skill when the user mentions ncmctl, 网易云音乐命令行, 网易云音乐, 网易云, 网易音乐, NetEase Cloud Music CLI, or asks how to install, login, download songs, upload to cloud, decrypt NCM files, convert ncm to mp3/flac, run daily tasks (sign/partner/scrobble), 刷歌, 云贝签到, 音乐合伙人, 黑胶签到, or use API debugging tools. Also trigger on questions about ncmctl configuration, cookie management, or Docker deployment. Use even if the user does not explicitly say "ncmctl" when the work is clearly related to NetEase Cloud Music CLI operations.
redis-cli
by chaunsinRedis command-line interface (redis-cli) reference and usage guide. Use this skill whenever the user mentions redis-cli, Redis CLI, or any task involving querying, inspecting, debugging, or managing Redis from the command line. Triggers on key/value reads and writes, SCAN or keyspace inspection, INFO or MONITOR troubleshooting, latency and bigkeys analysis, Pub/Sub, Lua scripts, ACL and client management, cluster operations, import/export, and Redis module workflows such as RediSearch, RedisJSON, Bloom/CF/TopK, vector search, and time series commands. Use even if the user does not explicitly say "redis-cli" when the work is clearly terminal-based Redis operations.
pre-release-review
by chaunsinProduction pre-release code review and deploy-readiness audit for web/backend services. Use this skill whenever the user mentions release audit, pre-release review, production deploy, deploy readiness, go-live review, code release, publishing to production, tag release, CI/CD before production, checking a PR before release, or asking whether code is safe to ship. This skill compares a PR or git range against the previous release point and looks for missing migrations, environment/config changes, seeded data, cache work, queues, object storage assets, credentials leaks, service deployment order, rollback risk, and other production launch blockers. Prefer this skill even when the user only says "review this release" or "check production risks".
rclone-cli
by chaunsinRclone command-line cloud storage manager reference and usage guide. Use this skill whenever the user mentions rclone, or any task involving terminal-based cloud file operations such as upload, download, sync, copy, move, mount, or remote management. Triggers on S3-compatible storage, cloud-to-cloud transfer, remote mounts, backups, filtering, bandwidth control, encrypted remotes, bisync, and workflows across providers such as S3, OSS, COS, OBS, MinIO, Google Drive, Dropbox, OneDrive, and Azure Blob. Use even if the user does not explicitly say "rclone" when the work is clearly command-line cloud storage management.
hugo-to-markdown
by chaunsinConvert Hugo documentation sites and Hugo-managed content into standard Markdown. Use when Agent needs to inspect a local Hugo repository, read hugo.toml or config files, content/, archetypes/, layouts/_shortcodes/, layouts/_markup/, and related docs content, then produce Markdown output with Hugo front matter, shortcodes, render hooks, Markdown attributes, ref/relref links, includes, page bundles, page resources, and asset paths resolved or downgraded safely. This skill is especially useful for Hugo docs migrations, Hugo-to-Markdown exports, and repository-specific conversions where the local Hugo configuration, embedded shortcode rules, and custom templates define the true rules.
qcc-check
by chaunsinAudit this go-qcc-sdk repository against official Qichacha QCC OpenAPI documentation for interface coverage by ApiCode. Use when the user invokes $qcc-check, /qcc:check, qcc:check, /qcc-check, or asks to compare local {ApiCode}.go implementations with https://openapi.qcc.com/dataApi docs, find missing interfaces, identify deprecated local ApiCodes, or generate a QCC SDK coverage report.
qcc-create
by chaunsinCreate or update Go SDK interfaces for Qichacha QCC OpenAPI docs. Use when the user invokes /qcc:create, qcc:create, /qcc-create, or asks to implement, audit, repair, or refresh one or more go-qcc-sdk APIs from QCC ApiCodes such as 886 or official URLs such as https://openapi.qcc.com/dataApi/886.
go-qcc-sdk
by chaunsinConsumer integration helper for developers using github.com/chaunsin/go-qcc-sdk in Go applications. Use this skill for 接入 go-qcc-sdk, 企查查 SDK or QCC SDK setup, credential loading, config, calling existing SDK methods, handling SDK errors, reading Response[T]/Result/Paging, app service wrappers, mock tests, local httptest fakes, and troubleshooting consumer-side usage. Not for modifying the SDK itself.
postgresql-cli
by chaunsinPostgreSQL interactive terminal (psql) reference and usage guide. Use this skill whenever the user mentions psql, PostgreSQL command-line client, backslash commands, meta-commands, \d commands, database inspection, SQL scripting in PostgreSQL, importing/exporting data with psql, \copy, psql formatting, psql variables, or any task involving connecting to or interacting with a PostgreSQL database from the terminal. Also applies when the user asks about PostgreSQL query execution, table inspection, schema exploration, database administration from CLI, or psql configuration and customization. Even if the user doesn't explicitly say "psql" but is working with PostgreSQL from the command line, this skill is relevant.
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.