3,999 件 ・ 関連度順
概要と使いどころ
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
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。
Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.
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。
Biological knowledge: query curated assertions and reference databases for genes, variants, pathways, regulation, cell types, reference atlases, perturbation-derived relationships, phenotypes, therapeutic targets, drugs, interactions, annotations, signatures, compounds, and predicted structures. Use for identifiers, marker sets, gene sets, associations, ontologies, database facts, and cross-database 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。
Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.
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。
Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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。
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
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。
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
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。
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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。
Guide for implementing smooth, native-feeling animations using React's View Transition API (`<ViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter/exit of components, animate list reorder, implement directional (forward/back) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.
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 entity behavior, subscriptions, lifecycle handling, and reusable action APIs.
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 reusable interactions, verbs, in-hand use, actions, and BUI requests with clear validation and mutation.
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。
Discover and reuse owner resource structure, then validate paths, RSI, audio, maps, attribution, and localization companions.
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。
Spawns vehicles on a running CARLA server and destroys them — scattered across the map's spawn points on Traffic Manager autopilot, a row queued in a single lane at a fixed spacing, or a single hero/ego vehicle (optionally named for native ROS 2 publishing with --ros-name). Covers blueprint filtering, the atomic autopilot hand-off, and cleanup. Use when the user asks to "spawn vehicles/cars/traffic", "add N cars driving around", "put cars in a lane 15 m apart", "spawn the ego/hero vehicle", or "remove the vehicles".
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。
Taxonomia de 86 layouts de apresentação/marketing (hero, pricing, auth, nav, CTA, testimonials, footer, FAQ, onboarding, blog, contact, stats, bento, states) adaptada do VibeUI. Consultar ANTES de construir landing pages, onboarding flows, auth screens, ou qualquer UI de camada de apresentação. Complementa o design-library/solutions (que cobre módulos verticais de produto).
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。
Evaluates and implements age estimation and verification technologies for online services. Covers facial age estimation, digital ID verification, self-declaration with risk assessment, AI-based age estimation, and the accuracy versus privacy tradeoff. Includes ICO guidance and euCONSENT framework. Keywords: age verification, age estimation, facial analysis, digital ID, children, online safety.
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。
Add Google Calendar integration to Deus. Agents can list, create, update, and delete calendar events. Guides through GCP OAuth setup, token generation, keep-alive timer, and CLI command installation.
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。
Kerberos 委派攻击（非约束/约束/RBCD）。当 BloodHound 发现委派配置、或已获取有 SPN 的服务账号/机器账号控制权时使用。通过 S4U 协议滥用可实现跨服务模拟任意用户，常用于域内权限提升和横向移动。
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。
Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.
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 identify and test for account takeover vulnerabilities in web applications. Use this skill whenever the user mentions account takeover, authentication bypass, password reset attacks, email verification bypass, session hijacking, or any technique to compromise user accounts. This includes testing authorization issues, unicode normalization attacks, reset token reuse, CORS/CSRF/XSS exploitation, cookie manipulation, and OAuth vulnerabilities.
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。
Bootstrap een klantproject voor 42-SEO-Skills. Vraagt naar profiel (content/ecom/technical/full), activeert alleen relevante skills via disabledSkills in .claude/settings.local.json, zet auth (.env) op, maakt de gedeelde Python venv in ~/.claude/venvs/42-seo/, en prikt 42-reports/<klant>/ neer. Use when user says "init SEO project", "42-init", "setup SEO", "nieuw klantproject", "initialiseer SEO".
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 you need framework-agnostic OpenAPI 3.x guidance — spec structure, metadata and versioning, paths and operations, reusable schemas, security schemes, examples, documentation quality, contract validation (e.g. Spectral), breaking-change awareness, and handoffs to codegen — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review an OpenAPI; Improve an OpenAPI; Improve API contract; Improve API schema design. Part of cursor-rules-java project
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。
Cloudflare-first platform selection. Decision trees for Workers, D1, R2, KV, DO, Queues, Vectorize, Containers, Sandboxes, Flagship, Agent Memory, Workflows v2. Default stack, override conditions, auth, data patterns, reliability.
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。
Raccourcir des URLs via is.gd ou v.gd. Use when: l'utilisateur demande de raccourcir une URL, créer un lien court, ou simplifier une URL longue. NOT for: URLs nécessitant une authentification, liens personnalisés avec domaine propre, analytics avancés.
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。