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概要と使いどころ
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
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Filter degenerate, uninformative inputs before statistical tests: single-sequence alignments, empty files, constant features, zero-variance inputs, all-NaN columns. See nan-safe-correlation for NaN-aware correlation; statistical-analysis for test guidance.
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omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.
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Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.
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protocols.io REST API: search and fetch wet-lab, bioinformatics, and clinical protocols by keyword, DOI, or category, with steps, reagents, materials, equipment, timing. Public access free; auth needed for private or publishing. Pair with opentrons-protocol-api or benchling-integration to execute.
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Get real-time stock quote with price, volume, change, market cap, and 52-week range for any ticker symbol. Use when user asks about current stock price, quote, or basic stock info.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Background legal and regulatory context for the shinkoku tax filing plugin. Contains the standard disclaimer about the scope of tax information provided, the relationship to the Tax Accountant Act (税理士法), and tool limitations. This skill is not user-invocable — Claude loads it automatically when generating tax-related responses that require a disclaimer.
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Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
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Complete RFI (Request for Information) management system. Create, track, route, and analyze RFIs with automatic notifications and response deadline tracking.
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Automatically classify and extract information from construction documents using NLP. Categorize RFIs, submittals, change orders, specifications, and contracts.
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Transform webinar recordings into multiple content assets including blog post series, social media snippets, infographic ideas, email sequences, and sales one-pagers. Extracts key moments, quotes, and insights for maximum content ROI. Use when users need to repurpose webinars, video content, or live presentations into multi-channel marketing materials.
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Master skill that transforms ANY content into 8+ formats: slides, infographic, video script, podcast, social posts, email series, flashcards, quiz, one-pager, interactive game.
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Probe memory/summary clarity via dual anchor questions: task progress, info gaps. Use when verifying session state or summary before handoff or compression.
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Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces.
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Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.
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Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence.
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Use when a pipeline DOT file has passed structural validation but may still have flow inefficiencies, spec fidelity gaps, or information loss in prompts. Use after dotfile-audit or dotfile-from-spec when you want to deeply verify a pipeline captures everything from the original spec. Use when prompts might be missing critical domain rules, data models, or invariants.
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Route DRL-Pytorch reinforcement-learning workflows for standalone PyTorch Q-learning, DQN-family, PPO, DDPG, TD3, SAC, Atari DQN, and Actor-Sharer-Learner scripts.
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Use DreamerV3 for world-model reinforcement learning training, embodied environment dataflow, JAX model internals, result operations, and troubleshooting.
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Use DreamerV2 2.2.0 for TensorFlow world-model reinforcement-learning training, custom Gym integration, typed experiment configuration, replay/checkpoint management, and JSONL/TensorBoard evaluation.
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Use ClawBio for local-first bioinformatics agent workflows: install and run its skill library, route genomic or omics inputs, create reproducible reports, connect optional MCP or Nextflow integrations, and author or validate skills.
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Use CleanRL's single-file reinforcement learning scripts, evaluation utilities, experiment operations, and repository maintenance workflows safely.
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Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.
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