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概要と使いどころ
Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.
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Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
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Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.7 Flash for quality review. Refines when the review requests improvement, with at most two generations. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
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Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.
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Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
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Prepares and launches nf-core/pacsomatic matched tumor-normal PacBio HiFi genomics workflows from unaligned BAM inputs. Supports samplesheet generation, pinned Nextflow launch artifacts, local checks, LSF/Slurm/PBS Pro/SGE launcher submission, and startup troubleshooting. Use for pacsomatic run preparation and execution, not general short-read somatic analysis or medical imaging PACS.
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Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
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Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
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Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.
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Predicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or sequence plus SMILES/SDF/MOL2. Covers batch docking, pose triage, confidence interpretation, and validation. Use for molecular docking and virtual-screening pose generation, not binding-affinity prediction.
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Generate images and iteratively edit saved image artifacts.
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Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.
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Scrape leads from multiple platforms using Apify Actors.
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Generate hand-drawn 16:9 article illustrations with the Grav character IP, sparse annotations, and absurd but clear visual metaphors.
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Geracao de imagens humanizadas via Google AI Studio (Gemini). Fotos realistas estilo influencer ou educacional com iluminacao natural e imperfeicoes sutis.
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Create custom AI subagents with proper plugin structure, persona generation, and companion routing skills.
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AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.
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Generate or edit images from text prompts. Use when the user asks to create, draw, design, or edit an image, illustration, photo, icon, poster, or any visual content.
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Automate changelog generation from commits, PRs, and releases following Keep a Changelog format. Use when setting up release workflows, generating release notes, or standardizing commit conventions.
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns. Use when creating API documentation, generating SDKs, or ensuring API contract compliance.
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The baseline method for building ONE stage — a single classroom — with this runtime. Covers how the plan is settled in conversation, the order the generation tools are called in, how the classroom roster is written, when narration has to be re-synthesized, and what must be true before a stage is called done. Load this before generating or rebuilding any stage from scratch, whatever its subject; it is the floor the topic skills build on, not an alternative to them.
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OpenMAIC assistant for setting up, generating, and extending OpenMAIC. Use when the user wants to use OpenMAIC, generate a multi-agent interactive classroom, or build on / extend / customize OpenMAIC and its @openmaic/* SDK (secondary development, 二开) — covers Live Demo or local setup, startup modes, provider keys, classroom generation, and secondary development (forking, providers/storage/themes, routes, or the renderer/editor).
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Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
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