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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agami-connect
by AgamiAIEnd-to-end database connection for agami: sets up credentials on first run (DB-type picker → writes ~/.agami/credentials.example for the user to fill in), then introspects the live DB directly into the agami semantic model (subject areas, tables, columns, relationships with join cardinality, deep-table column groups, sensitive-column flags) under <artifacts_dir>/<profile>/. The structural model is built deterministically by scripts/semantic_model (catalog mode, or a probe-mode fallback when the catalog is locked down); the skill then layers LLM enrichment (descriptions, entities, metrics) and seeds EXPLAIN-validated NL→SQL examples. Every model write is gated by the semantic-model validator — no breaking model is ever persisted.
agami-serve
by AgamiAIWires the local agami MCP server (mcp_server.py) into the Claude Desktop app in one step, so you can ask your database questions from Claude Desktop — not just inside Claude Code. Auto-detects the right Python interpreter (the one with your DB driver), copies the two self-contained server files to a stable ~/.agami/serve/ so the config survives plugin updates, and safely merges the entry into claude_desktop_config.json (backup + atomic write, preserving every other key). The local server is the mirror of the hosted Agami connector — same tools, local backend — so this is also how a developer feels the exact experience their business end-users would get.
agami-save-correction
by AgamiAISaves a user correction so future queries learn from it. Always appends a (question, corrected_sql) pair to the per-database examples YAML in the .agami home directory. Additionally, classifies the correction and — when applicable — applies a surgical edit to the OSI semantic model itself (relationship fix, field metadata, or new metric). Every model edit is OSI-conformant and validated before write; the validator is the binding gate. Shows the user a model diff for approval before any model mutation.
agami-review
by AgamiAIOpens the trust-layer review dashboard for the active profile's semantic model. Lists every entry needing review (Rule 1 metrics + named filters, plus Rule 2 entries below the confidence threshold) as cards with the source-signal block that produced each entry. The user replies in chat with structured commands (approve / reject / edit / threshold / done) to mark entries reviewed. Each approval writes back to the canonical YAML files in <artifacts_dir>/<profile>/ and runs the validator before promotion.
agami-reconcile
by AgamiAIReconciles a CSV of (label, expected_value) pairs from an existing dashboard against agami's answers. For each row, the skill generates a matching NL question, runs it through the active profile's semantic model, diffs actual vs expected, and surfaces matches in green and mismatches in red with drill-down receipts. The strongest onboarding demo for a skeptical data engineer — either we agree with their numbers (trust earned via evidence) or we surface a real definitional disagreement (trust earned via transparency).
agami-query-database
by AgamiAIAnswers natural-language questions about the user's database. Loads the OSI v0.1.1 semantic model and few-shot examples from the .agami home directory, generates SQL by composing OSI datasets/fields/relationships/metrics into a prompt (and reading Agami extensions for type info, choice fields, and performance hints), executes it locally via the user's chosen tool (psql / mysql / snowsql / sqlite3 native CLI, DuckDB binary, or the Python driver `execute_sql.py`), returns results as a markdown table with optional CSV export, and renders Chart.js HTML charts on request. All execution is local — no data leaves the machine.
agami-model
by AgamiAIOpens the model-explorer dashboard for the active profile's semantic model. Lets the user browse every schema, table, and field with live search, and queue Exclude / Include actions on tables and columns they don't want the runtime to use. Each action flips the entry's `agami.review_state` to `rejected` (exclude) or `unreviewed` (include) in the per-table YAML, gated by the validator and committed to the profile's git repo.
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.