4,274 件 ・ 関連度順
概要と使いどころ
Device pipeline analysis: feasibility and pivotal study stages, design-iteration cycles, RWE studies, and post-market obligations, with reimbursement-aware value framing and decision-track awareness across the med universe.
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 and scope production work with Odyssey's real-time interactive video world models (odyssey.ml — Odyssey-1/Odyssey-2/Starchild-1/Agora-1). Use when a request involves generating video that a user steers live with keyboard, text, speech, or controller input (streamed frames that respond in the moment), or when someone must decide between Odyssey-style interactive generation and offline clip generators (Sora/Veo/Kling) or downloadable 3D / game engines. Covers what is verified versus announced, current access routes, latency and coherence limits, feasible-now versus speculative use cases, pricing/access terms, and content/rights considerations. This is a fast-moving research-stage product; the skill teaches honest scoping, not a promise of production 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。
Provider-independent production guidance for translating measured audio features into deterministic video timing and motion. Use for beat-, onset-, phrase-, energy-, silence-, or spectrum-reactive visualizers, edits, typography, and generated compositions; not for music-video concept direction, audio generation, or a runtime-specific recipe.
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。
Provider-independent workflow for producing SaaS and software product demo videos, including app walkthroughs, feature launches, sales demos, onboarding clips, help-center explainers, website hero demos, and PLG assets. Use when an agent must plan, script, capture, synthesize, edit, localize, review, or QA privacy-safe software demo videos with substantiated claims, accessible captions, screen/cursor direction, CTA variants, and stakeholder approval.
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 structured design thinking through 5 progressive levels before any code is written. Levels: Capabilities, Components, Interactions, Contracts, Implementation. Use when building new features, refactoring significant code, designing modules, or when the user says 'design this', 'architect this', 'let's think before coding', 'walk me through the design', or 'whiteboard this'. For simple utilities enter at Level 4 (Contracts), for single-component tasks at Level 2 — see Complexity Calibration. Do not use for quick bug patches.
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。
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.
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。
Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin 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。
Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.
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。
Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
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。
Load gene expression count matrices from various formats including CSV, TSV, featureCounts, Salmon, kallisto, and 10X. Use when importing quantification results for downstream 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。
Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration 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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell 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。
Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.
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。
Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.
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 biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
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。
Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.
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。
Research products across Amazon, Google Shopping, and Xiaohongshu to understand pricing, reviews, market positioning, and consumer sentiment. Covers competitive product analysis, pricing benchmarks, feature comparison, and customer satisfaction signals.
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 and analyze pricing information from competitor websites. Identifies pricing tiers, features per tier, billing models, and positioning relative to market alternatives. Ideal for competitive pricing strategy and market positioning research.
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。
Research competitor pricing strategies by extracting pricing pages, comparing product listings on Google Shopping and Amazon, and analyzing market positioning. Produces pricing benchmarks, tier comparisons, and feature-per-price 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。
Build evidence-backed SEO keyword insights and strategy using SandBase-managed search, keyword, site-analysis, and SERP capabilities. Use when asked to discover or prioritize keywords, assess search demand or ranking feasibility, map intent and topics, find competitor gaps, plan organic-search landing pages or content, or analyze a website's SEO opportunities.
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 high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk 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。
Build Rust TUI apps with SuperLightTUI v0.20 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
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。
Build Rust TUI apps with SuperLightTUI v0.23 (immediate-mode terminal UI). Use this skill when the user asks to create, modify, or debug terminal UI code in this repo, or asks "how do I X in SLT / TUI / terminal", or types Korean triggers like "터미널 UI", "TUI 만들어줘", "SLT로", "ratatui 대신". Read REFERENCES.md for feature flags and doc pointers; grep `src/context/` and `src/widgets/` before inventing any API.
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 starting creative work (features, components, functionality, or behavior changes) to clarify user intent, requirements, and design before implementation.
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。