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 12 of 13 skills
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tmux

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Remote control tmux sessions for interactive CLIs (python, gdb, git add -p, etc.) by sending keystrokes and scraping pane output. Use when debugging applications, running interactive REPLs (Python, gdb, ipdb, psql, mysql, node), automating terminal workflows, interactive git commands (git add -p, git stash -p, git rebase -i), or when user mentions tmux, debugging, or interactive shells.

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schedule Updated 1 month ago
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duckdb

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DuckDB — the in-process, columnar, vectorized OLAP SQL engine shipped as a single zero-dependency binary ("SQLite for analytics"). Use when querying Parquet/CSV/JSON files directly with SQL and no load step, doing in-process analytical/OLAP queries and aggregations, using the `duckdb` CLI shell (interactive REPL or `-c`/`-json` one-shots for scripts and agents), writing "friendly SQL" (FROM-first, `SELECT * EXCLUDE`, `GROUP BY ALL`, `SUMMARIZE`, `PIVOT`), converting CSV↔Parquet↔JSON with `COPY`, reading remote data over HTTP/S3 via the httpfs extension, or `ATTACH`-ing a live Postgres/MySQL/SQLite database. Triggers on mentions of duckdb, the `duckdb` command, `.duckdb` files, replacement scans, `read_parquet`/`read_csv`, httpfs/`CREATE SECRET`, or "embedded analytics database". This is the DuckDB CLI and SQL dialect, NOT a generic SQL tutorial and NOT the DuckDB client libraries (Python/Node/Java — defer those to duckdb.org).

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schedule Updated 18 days ago
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k3s

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The k3s lightweight CNCF-certified Kubernetes distribution shipped as a single binary that bundles containerd, flannel, CoreDNS, Traefik, ServiceLB (Klipper), local-path-provisioner, and metrics-server. Use when installing or running a lightweight Kubernetes cluster, bootstrapping a single-node or HA k3s control plane, joining agent (worker) nodes, choosing embedded etcd vs an external SQL datastore, managing cluster/agent tokens, rotating certificates, taking or restoring etcd snapshots, enabling secrets encryption, doing airgap or private-registry installs, disabling bundled components (Traefik/ServiceLB/CoreDNS), or upgrading k3s. Triggers on mentions of k3s, `k3s server`, `k3s agent`, the `curl -sfL https://get.k3s.io | sh -` installer, `/etc/rancher/k3s/`, or Rancher's lightweight Kubernetes. This is the k3s CLI/distribution, NOT generic Kubernetes/kubectl.

navigation main article SKILL.md
schedule Updated 15 days ago
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fzf

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Command-line fuzzy finder for interactive filtering of any list. Use when interactively selecting files, searching command history (CTRL-R), creating selection interfaces in scripts, building interactive menus, or integrating fuzzy search with tools like ripgrep, fd, and git. Triggers on mentions of fzf, fuzzy finder, ** completion, interactive filtering, or shell keybindings CTRL-T/CTRL-R/ALT-C.

navigation main article SKILL.md
schedule Updated 22 days ago
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jq

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jq — the command-line JSON processor and its filter language for slicing, filtering, transforming, and reshaping JSON. Use when extracting fields or nested paths from JSON on the command line, filtering/`select`-ing arrays of objects, reshaping or building new JSON objects, emitting CSV/TSV with `@csv`/`@tsv`, getting raw unquoted strings for shell capture with `jq -r`, slurping (`-s`) and aggregating JSON (`add`, `group_by`, averages), parsing API responses, NDJSON pipelines (`-c`), or injecting shell values safely with `--arg`/`--argjson`/`$ENV`. Triggers on mentions of jq, the `jq` command, the jq filter language, `jq -r`, `.foo`/`.[]` filters, `@csv`/`@tsv`, slurp/`fromjson`, or "filter/transform JSON in the terminal". This is the jq CLI and filter language, NOT a generic JSON tutorial and NOT jq language bindings (jaq, gojq, PyPI `jq` — defer those to their own docs).

navigation main article SKILL.md
schedule Updated 18 days ago
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long-form-math

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Write mathematics in a long-form, understanding-focused style with detailed proofs and rich exposition. Use when explaining mathematical concepts, writing proofs, tutoring math, creating educational math content, or when the user asks for mathematical explanations. Inspired by Jay Cummings' Real Analysis and Chartrand's Mathematical Proofs. Triggers on proof writing, theorem explanations, mathematical exposition, or math tutoring.

navigation main article SKILL.md
schedule Updated 1 month ago
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fd

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A fast, user-friendly command-line tool for finding files and directories by name — a simpler, faster `find` replacement. Use when searching for files or directories by name or regex/glob pattern, filtering results by type, extension, size, or modified time, respecting .gitignore, or running a command per result with -x/-X. Triggers on mentions of the fd command, fdfind, "find files named", "search for a file", or replacing `find`. This is the fd CLI tool, NOT the file-search MCP server.

navigation main article SKILL.md
schedule Updated 22 days ago
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hledger

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Plain-text double-entry accounting with hledger. Use when recording transactions, checking balances, generating financial reports (balance sheet, income statement, cashflow), importing CSV bank statements, budgeting, tracking time, managing multiple currencies, or doing year-end closing. Triggers on mentions of hledger, ledger-cli, journal files, double-entry accounting, balance assertions, or plain-text accounting.

navigation main article SKILL.md
schedule Updated 1 month ago
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walkthrough-to-obsidian

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Convert game walkthroughs and guides from plain text into structured, interlinked Obsidian markdown pages. Use when the user wants to convert a walkthrough, FAQ, guide, or reference document into Obsidian vault pages. Triggers on mentions of converting walkthroughs, guides, or FAQs to Obsidian, or splitting a large text file into Obsidian pages.

navigation main article SKILL.md
schedule Updated 1 month ago
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chrome-cdp

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Interact with local Chrome browser session via Chrome DevTools Protocol. Use when asked to inspect, debug, or interact with a page open in Chrome, take screenshots of browser tabs, read accessibility trees, evaluate JavaScript, click elements, navigate pages, or automate browser interactions in a live Chrome session.

navigation main article SKILL.md
schedule Updated 1 month ago
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ansible

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ansible-core — the agentless, push-based, SSH automation engine that runs idempotent tasks from declarative YAML playbooks. Use when writing or running Ansible playbooks, issuing ad-hoc commands (`ansible -m`), managing INI/YAML inventory, authoring roles, installing collections with `ansible-galaxy`, encrypting secrets with Ansible Vault, tuning `ansible.cfg`, looking up module docs with `ansible-doc`, or provisioning/configuring hosts idempotently over SSH. Covers the ten `ansible*` CLIs, FQCN (`ansible.builtin.*`), become/privilege escalation, inventory patterns, variable precedence, check/diff mode, and strategies. Triggers on mentions of ansible, ansible-playbook, ansible-galaxy, ansible-vault, ansible-inventory, ansible.cfg, playbooks, roles, collections, or Ansible Vault. This is ansible-core (the engine + `ansible.builtin`), NOT generic configuration-management theory and NOT the bundled community `ansible` package's collection modules.

navigation main article SKILL.md
schedule Updated 22 days ago
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jj

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Jujutsu (jj) version control system - a Git-compatible VCS with novel features. Use when working with jj repositories, managing stacked/dependent commits, needing automatic rebasing with first-class conflict handling, using revsets to select commits, or wanting enhanced Git workflows. Triggers on mentions of 'jj', 'jujutsu', change IDs, operation log, or jj-specific commands.

navigation main article SKILL.md
schedule Updated 21 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.