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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conversation-dump
by QuantumBFSUse when analyzing conversation patterns — extracts dialog from Claude Code or Codex CLI history, classifies each user message across 6 academic dimensions (Bloom's cognitive level, Graesser question depth, Paul & Elder reasoning probe, Walton presupposition quality, Long & Sato discourse function, Graesser generation mechanism), and outputs tagged dialog reports
incarnate
by QuantumBFSUse when onboarding a contributor as an advisor — guides them through providing their background and conversation history, runs conversation-dump and soul-extraction, then synthesizes a named advisor profile for the ideas skill's advisor library
scaling-fit
by QuantumBFSUse when the user has L-indexed or size-indexed data and wants finite-size scaling — phrases like "fit a power law", "data collapse", "extract the critical exponent", "finite-size extrapolation", "extrapolate to the thermodynamic limit", "fit log L", "polynomial fit in 1/L", or "collapse these curves".
model
by QuantumBFSUse when the user names or describes a harness-tracked quantum lattice model. Match user prose to one of: - transverse-field-ising (TFIM): quantum-critical Ising chain / 2D Wilson-Fisher - heisenberg: SU(2) magnet, AFM or FM by sign of J - j1-j2: frustrated Heisenberg, J2/J1≈0.5 spin-liquid candidate - t-v: spinless fermions + NN repulsion (CDW vs Luttinger) - hubbard: t-U electrons; Mott transition, cuprate parent - t-j: strong-coupling Hubbard with no-double-occupancy - anderson-impurity (SIAM): impurity-in-bath, Kondo - multiorbital-hubbard: multi-band + Hund's J - spin-1-xxz: Haldane phase, AKLT - potts-clock: q-state, first-order / continuous / BKT by q Fires for each named model the user touches in a session, not just the first match.
physics
by QuantumBFSUse when the user asks a phase, mechanism, or diagnostic question — whether or not a specific model is named. Triggering topics: - criticality: second-order transitions, exponents, finite-size scaling - frustration: geometric or exchange-induced frustration - spin-liquid: fractionalized phases, topological order, RVB, "is this a spin liquid" - mott-transition: interaction-driven metal-insulator - kondo-effect: local-moment screening - confinement: gauge-theory confinement diagnostics Fires once per topic the user names, not once per session.
onboard
by QuantumBFSUse when the user is new to the harness, asks "where do I start" / "how do I use this" / "I'm new here", opens with an empty or unclear prompt, explicitly invokes `/onboard`, or starts a first session with no configured harness environment.
solve
by QuantumBFSUse when the user brings a concrete quantum many-body research problem — phrases like "ground state of Heisenberg N = 20", "is kagome a spin liquid", "Hubbard at U/t = 8", "compute the gap for TFIM at h = 1", "what does VMC give for J1-J2 at 0.5". Also fires when `/onboard` routes here or when the user pivots to a new problem mid-session.
parameter-scan
by QuantumBFSUse when the user wants to vary one or more parameters and see how a quantity responds — phrases like "how does X depend on Y", "sweep U/t from 0 to 10", "scan J2/J1 across the transition", "finite-size series at L = 12, 16, 20, 24", "bond-dimension sweep chi=50 to 400", a single-axis scan, or a multi-axis grid.
using-mpskit
by QuantumBFSUse when choosing or running MPSKit.jl for infinite / uniform matrix product states — VUMPS, IDMRG / IDMRG2, finite DMRG, TDVP — including unit-cell construction, the tangent-space gradient-norm convergence probe (calc_galerkin), symmetry setup via TensorKit, or MPSKit setup failures. MPSKit has no TEBD; route iTEBD to /using-tenpy.
using-tenpy
by QuantumBFSUse when choosing or running TeNPy (Python) for MPS calculations — especially iTEBD (the algorithm MPSKit lacks), plus iDMRG, VUMPS, finite DMRG/TEBD, and finite-T purification — including model setup, the tangent-space gradient-norm probe (tangent_projector_test), symmetry via conserve, threading, or TeNPy/numpy-ABI setup failures.
method-ltrg
by QuantumBFSUse when a finite-temperature Linearized Tensor Renormalization Group (LTRG) reproduction needs method-level route and tool selection — Trotterized classical tensor network from a quantum lattice model, layer-by-layer boundary contraction with SVD truncation, thermodynamic observables (free energy, internal energy, specific heat, susceptibility).
using-qmbcertify
by QuantumBFSUse when choosing or running QMBCertify.jl — the dedicated structured-NPA certifier for ground-state properties of 1D/2D (J1-J2) Heisenberg spin models, producing numeric certified lower/upper bounds on energy, correlations, structure factors, and partition functions via a Mosek SDP, with Gram-matrix export feeding an exact rational post-certification step (1D chains) — or for QMBCertify setup failures. One of the two step-2 handoff targets from /method-polyopt (the structure-exploiting one).
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.