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概要と使いどころ
Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
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。
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
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。
Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
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。
Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.
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 protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
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。
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
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。
Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
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。
End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.
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。
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
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。
Portable multi-source research with cross-source validation and an offline evidence ledger. Use for fact-checking, comprehensive research, or any question requiring multiple independent perspectives; work with the host agent's search tools and optionally add SandBase Tavily, Exa, Scholar, and Cloudsway coverage.
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。
Validate email addresses for deliverability, check reputation scores, verify MX records, and assess email-sending safety. Covers deliverability checks, reputation scoring, bounce risk assessment, and bulk validation for outreach campaign preparation.
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。
Apply vaex in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses vaex or its strengths fit the task.
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。
Apply validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.
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。
Apply umap-learn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses umap-learn or its strengths fit the task.
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。
Apply single-cell rna qc in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses single-cell rna qc or its strengths fit the task.
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。
Apply statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.
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。
Apply networkx in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses networkx or its strengths fit the task.
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。
Apply pytorch geometric in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses pytorch geometric or its strengths fit the task.
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。
Apply statistical power in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statistical power or its strengths fit the task.
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。
Apply shap in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses shap or its strengths fit the task.
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。
Apply sql optimization patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql optimization patterns or its strengths fit the task.
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。
Apply sympy in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sympy or its strengths fit the task.
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。
Apply power bi modeling in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses power bi modeling or its strengths fit the task.
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。
Apply polars in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses polars or its strengths fit the task.
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。