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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music-assistant-librespot-wrong-account
by ViktorBarzinFix for Music Assistant Spotify playback failing with "librespot does not support free accounts" even when the Spotify account has Premium. Use when: (1) Songs load for 1-2 seconds then auto-pause, (2) Music Assistant logs show "librespot does not support free accounts" followed by FFmpeg "Invalid data found when processing input" exit code 183, (3) Spotify provider shows "Successfully logged in" but streaming fails. Root cause is stale librespot credential cache pointing to a different (free-tier) Spotify account.
home-assistant
by ViktorBarzinControl Home Assistant smart home devices and automations. Use when: (1) User asks to turn on/off lights, switches, or devices, (2) User asks about the state of sensors, devices, or entities, (3) User says "turn on the lights", "set temperature", "lock the door", (4) User asks to run a scene or script, (5) User asks "what devices are on?" or "is the door locked?", (6) User mentions smart home, IoT, or home automation. There are TWO Home Assistant deployments: ha-london (default) and ha-sofia. Always use Home Assistant for smart home control.
crowdsec-agent-registration-failure
by ViktorBarzinFix CrowdSec agent pods stuck in CrashLoopBackOff after LAPI restart due to stale machine registrations. Use when: (1) CrowdSec agent init container fails with "user already exist" error during cscli lapi register, (2) agent pods show hundreds of init container restarts, (3) LAPI was restarted or redeployed but agents kept running with old credentials, (4) cscli machines list shows stale entries for current agent pod names. Covers deleting stale registrations to allow re-registration.
local-llm-gpu-selection
by ViktorBarzinGuide for selecting GPUs and hardware for local LLM inference on Dell R730 and comparing to Apple Silicon alternatives. Use when: (1) user asks about running local models (Ollama, llama.cpp), (2) user asks which GPU to buy for LLMs, (3) user wants to compare local models to Claude for coding, (4) user asks about quantized model selection, (5) user asks about Mac Mini/Studio vs GPU server for LLMs. Covers VRAM requirements, memory bandwidth as key metric, R730 GPU compatibility, multi-GPU considerations, and realistic quality comparisons to Claude models.
nfsv4-idmapd-uid-mapping
by ViktorBarzinFix for all file UIDs showing as 65534 (nobody) inside Kubernetes containers when using NFS volumes from TrueNAS/FreeBSD. Use when: (1) ls -lan inside a container shows all files owned by 65534:65534 despite correct ownership on the NFS server, (2) PostgreSQL fails with "data directory has wrong ownership", (3) chown inside containers returns "Invalid argument" on NFS volumes, (4) services that check file ownership (PostgreSQL, MySQL) crash on startup, (5) the same NFS mount shows correct UIDs on the host but 65534 inside containers, (6) NFSv4.2 appears in container mount output even though host mounts use NFSv3. Root cause: Kubernetes inline NFS volumes auto-negotiate NFSv4.2 (not NFSv3), and NFSv4 idmapd fails to map UIDs when domains don't match or users don't exist on the server.
clickhouse-k8s-nfs-system-log-bloat
by ViktorBarzinFix for ClickHouse consuming excessive CPU (500m-1000m+) on Kubernetes when running on NFS storage, caused by unbounded system log table growth triggering continuous background merges. Use when: (1) ClickHouse burns ~1 CPU core with no active user queries, (2) system.merges shows constant merge activity on system.metric_log or system.trace_log, (3) system log tables (metric_log, trace_log, text_log, asynchronous_metric_log) have grown to gigabytes while actual user data is tiny, (4) ClickHouse crashes with exit code 76 (loadOutdatedDataParts SIGSEGV), (5) attempting to mount custom config.d XML via Kubernetes ConfigMap causes exit code 36 (BAD_ARGUMENTS) crashes. Also covers why ClickHouse's MergeTree engine performs poorly on NFS and the CronJob workaround for system log truncation.
traefik-rewrite-body-troubleshooting
by ViktorBarzinTroubleshooting guide for the Traefik rewrite-body plugin (packruler/rewrite-body). Covers two failure modes: (1) Compression failure — plugin logs "flate: corrupt input before offset 5" when backends send gzip-compressed responses, corrupting response bodies and breaking WebSocket connections, authentication flows, and mobile app connectivity. (2) Silent skip — plugin silently skips content injection (rybbit analytics, trap links, or any HTML rewriting) when the request Accept header doesn't contain "text/html" (e.g., curl's default Accept: */*), making it appear broken despite correct configuration.
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.