「biology」の検索結果

Think and work like an expert Biomaterials Scientist. Use when a task calls for Biomaterials Scientist judgment. Reasons from material-biology interfaces, degradation-product toxicity, and mechanical mismatch to host tissue through ISO 10993 biological evaluation plans, ISO 10993-12 extract conditions, GPC/ICP/SEM characterization, and sterilization validation while treating endotoxin contamination, pH shift from acidic degradation, extract-ratio artifacts, and non-final-form test articles as first-class failure modes.

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Think and work like an expert Comparative Medicine Researcher. Use when a task calls for Comparative Medicine Researcher judgment. Reasons from species biology, translational validity, and the 3Rs through model-validity frameworks, IACUC protocols, ARRIVE 2.0 reporting, and FELASA/AALAS health surveillance while treating substrain drift, subclinical colony infection (murine norovirus, pinworm, Mycoplasma), analgesia-pathway confounds, and unstated husbandry variables as first-class failure modes.

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Think and work like an expert Clinical Embryologist. Use when a task calls for Clinical Embryologist judgment. Reasons from gamete and embryo biology, manufacturing-quality lab control, and prespecified cycle/oocyte/embryo denominators through Vienna consensus KPIs, Gardner/ASEBIR grading, time-lapse morphokinetics, WHO 6th-edition andrology, and vitrification SOPs while treating media-lot and incubator-gas drift, witness mix-ups, abnormal fertilization (1PN/3PN), and clinical case-mix confounding as first-class failure modes.

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Think and work like an expert Agroecologist. Use when a task calls for Agroecologist judgment. Reasons from socio-ecological farm systems, functional diversity, soil-biology-mediated fertility, and yield-service trade-offs through mother-baby and mixed-model trials, N/P/C mass balances, PLFA/amplicon and pollinator surveys, and NDVI/FRAGSTATS landscape analysis, while treating edge-effect and plot-size artifacts, weather-year confounding, surface-only soil carbon, and ignored labor/tenure adoption constraints as first-class failure modes.

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Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.

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Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.

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Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.

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Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

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Biology research tools reference. Always available inside agent containers.

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Structure retrieval, confidence-aware AlphaFold DB usage, coordinate download, PAE and pLDDT interpretation, and structure-guided biological annotation.

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Workflow for constraint-based metabolic modeling, context-specific models, gene essentiality, and systems-level interpretation.

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Patterns for building, maintaining, and scaling bioinformatics workflows. Covers Nextflow, Snakemake, WDL/Cromwell, container orchestration, and best practices for reproducible computational biology. Use when ", " mentioned.

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Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-chat-agents skill.

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Use this skill whenever reasoning about immunology, host-pathogen interactions, innate immunity, Toll-like receptors (TLRs), or experimental design in molecular biology. It is also highly relevant for scientific career strategy, particularly when discussing independence, pivoting research domains, or using reverse genetics (like knockout mice) for discovery. Channel Shizuo Akira, immunologist and pioneer of pattern-recognition receptors, to apply rigorous experimental standards (e.g., demanding chemically synthesized mimics over purified extracts) and to view innate immunity as a highly specific, compartmentalized system rather than a primitive defense.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S. Lander (geneticist, Broad Institute, Human Genome Project) provides a framework for hypothesis-free discovery, structuring "big science," and treating biology as an information science. Reach for this when the user is discussing open science, collaborative ecosystems, CRISPR/gene editing ethics, managing massive datasets, or setting realistic timelines for technological breakthroughs. Apply his principles to avoid the "lone genius" myth, prevent overpromising, and build foundational infrastructure that serves the broader community.

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This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data collection.

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Applies the philosophical reasoning of Mary Midgley (British moral philosopher). Use this skill when analyzing ethical dilemmas involving animals, environmental policy, the limits of science (scientism/reductionism), or when users are trapped by unhelpful metaphors (like 'the selfish gene' or 'humans as machines'). Trigger this whenever evaluating the intersection of biology and human nature, resolving irreconcilable viewpoints, or diagnosing systemic conceptual blockages in society or organizations. Midgley's approach emphasizes holism, emotional fellowship, and viewing philosophy as practical 'plumbing' rather than abstract logic.

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Carbon chemistry, functional groups, IUPAC nomenclature, isomerism, reaction mechanisms (substitution, elimination, addition), polymers, and biochemistry basics. Covers hydrocarbon classes (alkanes, alkenes, alkynes, aromatics), common functional groups (alcohols, aldehydes, ketones, carboxylic acids, esters, amines, amides), stereochemistry, condensation and addition polymerization, and the four biomolecule classes (carbohydrates, lipids, proteins, nucleic acids). Use when analyzing organic structures, predicting reaction products, or connecting chemistry to biology.

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CatColab Regulatory Networks - signed graphs for molecular biology modeling gene regulatory networks with positive (activating) and negative (inhibiting) edges.

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Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.

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