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.
Querying local SQLite index...
patent-landscape-tools
by DrugClawDrug-patent landscape workflow guide for searching US patents via the PatentsView API, classifying pharmaceutical claim types (NCE, formulation, method-of-use, polymorph, combination, biologic, process), grouping by patent family and assignee, estimating expiry timelines, and cross-referencing the FDA Orange Book for marketed-drug exclusivity windows. Use when the user asks about patent coverage, IP white-space, patent cliffs, or competitive filing activity around a drug, target, or compound class without asking for legal counsel.
find-skills
by DrugClawFind reusable skills from the vercel-labs/skills registry (especially by task keywords), evaluate fit, and suggest how to install/adapt them for DrugClaw.
grn-tools
by DrugClawGene regulatory network workflow guide for transcriptomics and single-cell expression matrices using Arboreto, GRNBoost2, and GENIE3. Use when the user asks to infer transcription factor-target links, score regulatory edges, or build a GRN from bulk or single-cell expression data.
variant-analysis-tools
by DrugClawVariant and VCF workflow guide for local SNV, indel, and structural-variant summarization, filtering, and consequence triage. Use when the user asks to inspect a VCF, count mutation classes, filter by VAF or depth, summarize genes or consequences, or prepare a local variant report before downstream annotation.
clinical-research-tools
by DrugClawClinical research workflow guide for protocol design, endpoint selection, evidence grading, reporting-guideline selection, statistical planning, and clinical-trial evidence synthesis. Use when the user asks to design or review human-subjects research, trial analyses, observational studies, study protocols, CSRs, or clinical evidence summaries without requesting patient-specific diagnosis or treatment decisions.
medical-data-tools
by DrugClawMedical data workflow guide for DICOM metadata inspection and basic de-identification, physiological signal analysis with NeuroKit2, and cohort-table profiling for clinical research datasets. Use when the user asks to inspect imaging metadata, summarize ECG/PPG/EDA/RSP/EMG signals, or profile tabular medical datasets without making patient-specific diagnoses or treatment decisions.
medical-qms-tools
by DrugClawMedical quality-system and documentation workflow guide for ISO 13485, FDA QMSR, design controls, risk management, CAPA, document control, supplier qualification, complaint handling, and audit preparation. Use when the user asks to plan, review, or gap-assess medical-device or diagnostic quality documentation without asking for legal determinations or regulatory guarantees.
chem-tools
by DrugClawComputational chemistry workflow guide for DeepChem, PySCF, RDKit, assay-table normalization, PDBbind-style structure datasets, QSAR and structure benchmarks, DrugBank lookup, ligand-only and structure-aware affinity prediction, ADMET triage, bioactivity prediction, virtual screening, and docking follow-up.
docking-tools
by DrugClawMolecular docking workflow guide and reusable pipeline template for AutoDock Vina, Open Babel, and PyMOL.
pharma-db-tools
by DrugClawQuery public drug-discovery and translational-research databases including PubChem, ChEMBL, BindingDB, openFDA, ClinicalTrials.gov, and OpenAlex. Use when the user asks to look up compounds, measured binding affinities, regulatory labels or adverse events, clinical trials, or drug-discovery literature from public APIs and curated exports.
pharma-ml-tools
by DrugClawPharmaceutical machine-learning workflow guide for library profiling, molecular featurization, benchmark dataset fetch, medicinal-chemistry filtering, and optional pose-generation handoff. Use when the user asks for datamol, molfeat, PyTDC, medchem, compound-library triage, dataset preparation, or chemistry-ML baselines beyond simple descriptor calculation.
bayesian-optimization-tools
by DrugClawBayesian optimization workflow guide for experiment suggestion, condition tuning, and closed-loop parameter search with Gaussian-process surrogates. Use when the user asks which experiment to try next, how to tune reaction or assay conditions, or how to balance exploration versus exploitation over a bounded numeric search space.
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.