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概要と使いどころ
Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon selection.
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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.
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 choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
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。
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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 Ruby gems following Andrew Kane's proven patterns and philosophy. Use when creating new Ruby gems, refactoring existing gems, designing gem APIs, or when the user wants clean, minimal, production-ready Ruby library code. Triggers on requests like "create a gem", "write a Ruby library", "design a gem API", or mentions of Andrew Kane's style.
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。
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
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。
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
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。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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。
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
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 protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. 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。
Multi-agent collaboration bringing 2-4 perspectives into one discussion for architecture decisions and trade-off analysis. Use for 'should we X or Y', design review, or debate before deciding.
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 UI components/design systems, review visual design, check accessibility. Triggers: design system, color palette, font pairing, WCAG check. Data-driven: palettes, fonts, UX guidelines.
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。
Read-first Figma gateway via Figma MCP: analyze designs, implement 1-3 screens, extract tokens, produce Evidence Packet. Advanced ops gated on explicit intent. Use for 'figma', 'design tokens'.
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。
Use when designing UI components, reviewing visual design, building design systems, or checking accessibility. Auto-activates on frontend design tasks and UI reviews.
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。
Bootstraps a new project end to end: research, design, plan, scaffold, implement, docs. Explicit invocation only. NOT for autonomous builds (mk:autobuild); NOT for existing-repo work (mk:cook).
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。
Use this skill to generate well-branded interfaces and assets for MeowKit (AI Agent Toolkit for Claude Code), either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for prototyping.
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 and review durable REST, GraphQL, RPC, or event API contracts: shape, compatibility, pagination, error semantics. Discovers existing consumers first. Not implementation, schema, or release.
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。
Use when designing UI components, creating design systems, reviewing visual design, or checking accessibility compliance. Triggers on "design system", "UI review", "color palette", "font pairing", "accessibility audit", "responsive design", "WCAG check". Auto-activates when ui-ux-designer agent is working. Data-driven: 160 palettes, 73 font pairings, 161 product types, 98 UX guidelines, 25 chart types.
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。
Multi-agent collaboration session. Brings 2-4 agent perspectives into one discussion for architecture decisions and trade-off analysis. Use when asked "should we X or Y?", "let's discuss", or "design review".
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。
Serve any markdown file or directory as a calm, book-like reader via a local HTTP server (127.0.0.1 only). Best for long-form documents — RFCs, runbooks, design docs, plan files — where comfortable in-browser reading matters. Supports Mermaid diagrams, dark/light mode, plan navigation sidebar, and keyboard shortcuts. Requires explicit start and stop lifecycle.
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。
Use when designing UI components, reviewing visual design, building design systems, or checking accessibility. Auto-activates on frontend design tasks and UI reviews.
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。
Read-first Figma gateway via Figma MCP: analyze designs, implement Figma-to-code for 1–3 screens, extract design tokens, produce a Figma Evidence Packet for planning handoff, screenshot fallback. Advanced operations (Code Connect, canvas writes, design-system/library patterns) are gated references loaded only on explicit intent. Triggers: 'figma', 'design link', 'implement this design', 'design tokens', 'produce a Figma Evidence Packet'.
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 and review durable REST, GraphQL, RPC, or event-facing API contracts. Use for public or shared API contracts, endpoint/schema consistency, compatibility, pagination, error semantics, and API documentation. Discovers existing consumers and conventions before recommending a style.
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。