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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dynamo-lap-cell-fate-transition
by aristoteleoCompute least action paths (LAP) between hematopoietic or general cell types in a dynamo vector-field AnnData, then rank transcription factors by MSD along each path and evaluate predictions via ROC analysis. Use when running dyn.pd.compute_cell_type_transitions, predicting optimal cell fate conversion trajectories, prioritizing transcription factor cocktails for cell reprogramming, or reproducing the 501_lap_tutorial.ipynb workflow on scNT-seq or metabolic-labeling data.
dynamo-pseudotime-velocity
by aristoteleoConvert pseudotime into reusable `dynamo` RNA velocity outputs on an `AnnData` object, then optionally continue into vector field, topology / potential, fate, and animation without relying on measured spliced/unspliced kinetics. Use when adapting the `201_dynamo_beyondvelo.ipynb` tutorial, working from pseudotime plus a neighbor graph and embedding, or choosing between `pseudotime_velocity` method branches such as `hodge`, `gradient`, and `naive`.
dynamo-one-shot-total-rna-velocity
by aristoteleoRun or adapt a one-shot total RNA velocity workflow in `dynamo` for metabolic-labeling or scNT-seq `AnnData`, including monocle preprocessing with an optional curated gene list, grouped moments by labeling time, Model-2 `dynamics`, `calculate_velocity_alpha_minus_gamma_s`, low-dimensional projection with `cell_velocities`, and optional streamline or phase-portrait plotting. Use when converting tutorials such as `301_tutorial_hsc_velocity.ipynb`, or when choosing between `one_shot_method` branches like `sci_fate` and `combined` and projection `method` branches like `cosine` and `pearson`.
dynamo-lineage-appearance-analysis
by aristoteleoCompare lineage appearance timing and its regulators on a precomputed `dynamo` vector-field `AnnData` using topography, graph potentials, Jacobian, and vector-calculus outputs. Use when checking whether one lineage appears earlier than its peers, curating fixed points, analyzing regulator pairs on a downstream-ready vector field, or adapting `400_tutorial_hsc_dynamo_megakaryocytes_appearance.ipynb`.
dynamo-conventional-rna-velocity
by aristoteleoRun or adapt a conventional spliced/unspliced RNA velocity workflow in `dynamo`, including `Preprocessor` preprocessing, `dynamics`, low-dimensional `cell_velocities`, `VectorField`, topology / potential analysis, confidence-based correction, fate prediction, and optional animation. Use when analyzing conventional scRNA-seq `AnnData`, reproducing or adapting tutorial notebooks such as `200_zebrafish.ipynb`, or selecting between preprocessing, kinetics, vector-field, and fate stages for a reusable velocity pipeline.
dynamo-differential-geometry-analysis
by aristoteleoRun downstream differential-geometry analysis on a `dynamo` vector-field `AnnData`, including velocity, acceleration, curvature, Jacobian, regulatory-network, ddhodge pseudotime, and state-graph branches. Use when adapting the `403_Differential_geometry.ipynb` tutorial, extending a conventional spliced/unspliced RNA velocity workflow into vector calculus, or choosing among `method`, `mode`, `sampling`, `formula`, `adjmethod`, or `gene_order_method` branches.
dynamo-geneid-convert
by aristoteleoConvert Ensembl-style gene IDs to gene symbols in `dynamo` with `dynamo.preprocessing.convert2gene_symbol` or `dynamo.preprocessing.convert2symbol`, including human and zebrafish IDs, version-suffix stripping, `AnnData.var_names` updates, and optional preprocessing handoff. Use when adapting `docs/tutorials/notebooks/110_geneid_convert_tutorial.ipynb`, standardizing `adata.var_names`, mapping Ensembl IDs to symbols, or doing identifier cleanup before a `Preprocessor` recipe.
dynamo-in-silico-perturbation
by aristoteleoPerform in silico gene perturbation on a dynamo vector-field AnnData to predict cell fate diversion after single or multi-gene activation or suppression, then visualize the results with streamline or quiver plots. Use when running dyn.pd.perturbation, predicting transcription factor perturbation effects, simulating gene knockdown or overexpression in scRNA-seq data, reproducing 502_perturbation_tutorial.ipynb, or choosing among pertubation_method, perturb_mode, and emb_basis branches.
cell-segmentation-skills-index
by aristoteleoCell and nucleus segmentation tools for microscopy images. Covers Cellpose, SAM-based methods, StarDist, InstanSeg, and Mesmer.
figure-styling-skills-index
by aristoteleoAesthetic guidelines and output-type recipes for scientific figure production. Supports lightweight default-agent use through SKILL.md + one outputType recipe, with optional venue-specific style guides when requested.
liveview-skills-index
by aristoteleoSkills for opening and driving agent-controllable visualization components in the Pantheon UI sidebar — interactive viewers the agent can open, control, and read back. Viewers: Vitessce (spatial / single- cell omics), Viv (bioimage / microscopy), volume3d (3D image volumes — MIP/ISO), spatial3d (3D spatial transcriptomics), Mol*, IGV, Gosling, Cytoscape, MSA, RDKit, phylotree, plus agent-generated apps.
database-access-skills-index
by aristoteleoSkills for querying and downloading data from genomic, transcriptomic, 3D-genome, and cancer-genomics databases. Covers programmatic access to public repositories, gene annotation, sequence retrieval, processed functional-genomics tracks, Hi-C / Micro-C contact matrices, TCGA-style cohorts, and large-scale single-cell data.
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.