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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vault-curator
by ssdavidaiProcess raw inbound content (emails, voice memos, notes) into structured Obsidian vault records with proper frontmatter, wikilinks, and file placement.
vault-distiller
by ssdavidaiRead operational vault records and extract latent knowledge into structured learning records with proper frontmatter, wikilinks, and file placement.
vault-janitor
by ssdavidaiFix vault quality issues — broken frontmatter, invalid values, orphaned records, garbage content.
alfred-channel-attachments
by ssdavidaiHow to read files (audio, documents, images) that Sir sent to Alfred on any channel — Slack file uploads, Telegram voice memos, MMS audio, generic URLs. Converts file references into transcripts + vault records so Alfred doesn't say "I don't see your file".
alfred-channel-delivery
by ssdavidaiHow to deliver a message to Sir on a specific channel (Slack DM, Telegram, SMS, voice call, email) using cached contact IDs from KNOWN_CONTACTS.md instead of walking workspace directories. Read this whenever Sir says "send/text/call/email me…".
alfred-chore-authoring
by ssdavidaiCreate new chores or edit existing ones on Sir's request. A chore is a recurring (or one-shot) Temporal workflow Sir has asked you to set up. This skill teaches the full lifecycle — design the right pattern (agent-driven vs Python-pipeline), author the workflow, register it, test it, and iterate. Use whenever Sir says "set up a chore to…", "every morning remind me to…", "automate X", or asks you to change an existing chore.
alfred-chore-management
by ssdavidaiInspect, trigger, pause, and reason about Sir's scheduled chores. Chores are recurring Temporal workflows — some shipped as platform built-ins, some bespoke Python generated for Sir during onboarding. Use whenever Sir asks about what's running on a schedule, wants to manually trigger a chore, pause/unpause one, or check why a chore hasn't produced results.
alfred-connected-apps
by ssdavidaiConversational management of Sir's Composio-connected third-party apps — list, connect (OAuth + API key), reconnect on expiry, disconnect, and inspect capabilities. Covers the full OAuth lifecycle including the 1h grace window after reconnect.
alfred-daily-briefing
by ssdavidaiAssemble and deliver Sir's morning briefing as a continuous narrative — ground in last night's digest, ingest overnight inputs, reason about how matters moved, then write a butler's note. Invoked at Sir's local morning by the chore system. Output is BOTH a vault-persisted record (event/daily-brief-<date>.md) and the Slack message Sir sees at breakfast.
alfred-daily-digest
by ssdavidaiAssemble and deliver Sir's evening digest — a backward-looking close of the day that picks up where this morning's brief left off. Reports per-matter outcomes vs expectations, what's still open, and what tomorrow needs to be ready for. Invoked at Sir's local evening (default 19:00 CET / 17:00 UTC) by the chore system. Output is BOTH a vault-persisted record (event/daily-digest-<date>.md) and the Slack message Sir reads as he winds down.
alfred-destructive-ops
by ssdavidaiHow Alfred performs container restarts, env-var / credential edits, backup triggers, and other destructive ops — with explicit approval doctrine. The operations in this skill CAN'T be undone or have significant blast radius; read the approval flow before invoking any endpoint here.
alfred-email-channel
by ssdavidaiHow to respond (or not respond) to inbound email on Sir's Alfred inbox. Covers reply vs reply-all vs forward, context assembly, authorized senders, and the channel vs stream distinction.
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.