381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 9 of 9 skills
leopu00

circles-and-sources

by leopu00
star 1

Strategy map for what to search WHERE, derived entirely from the candidate profile. The 5 concentric circles (work_mode + relocation) tell you the geographic scope; the 4 source tiers (LinkedIn → ATS aggregators → niche → web) tell you which platforms to drain in order. A scout that searches the wrong tier in the wrong circle wastes its quota and its `scout-coord` partition. Open this skill at boot (after `scout-coord`) and again whenever a circle is exhausted or a `[FEEDBACK]` from the Analyst suggests changing source.

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schedule Updated 19 days ago
leopu00

throttle

by leopu00
star 1

Pause your loop for N seconds in a tracked way. ALWAYS use this instead of `sleep` whenever you want to slow down your iteration rate to respect the team's rate budget. The duration is read from $JHT_HOME/config/throttle.json (the Captain calibrates per-agent values there); pass --agent <your-name> and the skill resolves the rest. Uses a detached-child pattern that survives any provider tool-call timeout (Kimi 60s, Codex 30s, Claude 120s/600s). Always pair with `jht-throttle-check` before each task to recover if a parent gets killed prematurely. Logs every pause to $JHT_HOME/logs/throttle-events.jsonl. `sleep` for throttle pauses is FORBIDDEN.

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

cv-disk-audit

by leopu00
star 1

Healthcheck periodico (Dottore) per riconciliare CV su disk e cv_pdf_path nel DB. Identifica orfani (file su disk senza riga DB) e ghost (riga DB con cv_pdf_path che punta a file inesistente). Notifica il Capitano sui mismatch così l'utente non perde top PASS invisibili e non vede "CV da scrivere" per CV già scritti.

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schedule Updated 19 days ago
leopu00

scout-web-access

by leopu00
star 1

Strato web-access cross-provider per gli Scout (F-2). 5 componenti coordinati — anti-bot robust scrape, LinkedIn session + search, email IMAP poll, multi-Scout workspace claim, freshness focus. Usato come stack base per ogni sweep: lo Scout sceglie il livello di accesso più leggero che funziona, e sale solo quando bloccato.

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

liveness-check

by leopu00
star 1

Diagnose whether a team agent's tmux session is alive, in a long turn, or silently dead — and respawn it preserving context if dead. Owned by the Dottore (the team's roving health-check agent), not by the Captain. The core failure mode this skill catches: `jht-tmux-send` returns `exit 0` even when the target CLI has crashed (the message is written into a bare bash, then lost). Without periodic liveness checks the team keeps "talking to a corpse" and the Captain counts on actions that will never happen.

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

profile-schema

by leopu00
star 1

Single source of truth dello SCHEMA del candidate_profile.yml — il formato canonico che TUTTO il team produce e consuma. Modello a 3 livelli: core congelato + blocchi standard + blocchi custom liberi. Definisce i 6 `kind` di blocco che il web sa rendere e la regola di governance (nessun agente inventa il formato). Ogni write del profilo va validato con `jht profile validate`. Riferita da profile-yaml, onboarding-flow, parse-cv, cv-structure.

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

spawn-doctor

by leopu00
star 1

Spawn a fresh DOTTORE on-demand when you (Capitano/Assistente/Sentinella/Mentor) need an immediate health-check round. Use this INSTEAD of writing to the DOTTORE session when the user asks "fai partire il dottore" / "dottora" / "controlla il team", because between scheduled rounds the DOTTORE session is bash residua (one-shot lifecycle, ~10 min active + ~110 min sleeping for next 2h-cycle spawn).

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

emergency-handling

by leopu00
star 1

How to handle rate-limit emergencies and the FATAL cascade when the bridge goes blind. Includes the cooldown-bypass triggers, the L4-SOFT/L5-HARD recovery path, and the RESET SESSIONE handling on a >30-point usage drop.

navigation main article SKILL.md
schedule Updated 19 days ago
leopu00

mentor-output

by leopu00
star 1

How the Mentor speaks once a pattern from `mentor-patterns` has crossed the threshold. Three output formats — strategic advice (rare, weighty), weekly digest, on-demand answer — each with strict shape and voice rules. The Mentor's authority comes from how rarely the words come and how heavy each one weighs; this skill enforces that. Owned by the Mentor. Pair with `chat-web` (delivery via jht-send) and `mentor-patterns` (the trigger).

navigation main article SKILL.md
schedule Updated 19 days ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.