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概要と使いどころ
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.
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Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation by nonlinear regression (and reports Lineweaver-Burk for reference). Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor. NOT for BRENDA database lookups of published constants (use the BRENDA tools).
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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.
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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
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Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.
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Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.
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Move entities through containers, hands, slots, storage, equipment, and inventories without corrupting ownership or transforms.
日本語の概要は準備中です。原文の説明を表示しています。
Spawn, initialize, replace, transfer, and delete entities without breaking lifecycle, transforms, containers, or ownership.
日本語の概要は準備中です。原文の説明を表示しています。
Video and audio processing capabilities for Flutter applications
日本語の概要は準備中です。原文の説明を表示しています。
Write model cards documenting AI capabilities. TRIGGERS - Use when user needs help with ai-model-card-writer related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Design planning capabilities for AI agents. TRIGGERS - Use when user needs help with ai-agent-planning related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Plan agritourism activities and marketing. TRIGGERS - Use when user needs help with agritourism-plan related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Design AI assistant personalities. TRIGGERS - Use when user needs help with ai-assistant-personality related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Design after-school program activities and structure. TRIGGERS - Use when user needs help with after-school-program related tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Generate an editable DOCX service agreement with parties, terms, and payment schedule.
日本語の概要は準備中です。原文の説明を表示しています。
Extract address, listing price, bedrooms, bathrooms, amenities, and agent contact details from a public listing page.
日本語の概要は準備中です。原文の説明を表示しています。
Extract line items, quantities, unit prices, delivery dates, and supplier details from purchase order documents.
日本語の概要は準備中です。原文の説明を表示しています。
Extract SKUs, product names, unit prices, availability, and minimum order quantities from a supplier catalog page.
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Extract landlord, tenant, property, lease dates, rent, deposit, utilities, occupants, and pet terms from residential leases.
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Extract parties, obligations, restrictions, permitted disclosures, and expiry dates from non-disclosure agreements.
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Extract headline, author, publication date, summary, category, and named entities from a public news article.
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Extract shipment details, item quantities, and delivery confirmation data from warehouse delivery notes and goods received notes.
日本語の概要は準備中です。原文の説明を表示しています。