1.1万 件 ・ 関連度順
概要と使いどころ
When the user wants to implement, optimize, or use App Clips for app discovery and conversion. Use when the user mentions "App Clip", "app clip code", "mini app", "instant app", "App Clip card", "App Clip link", "no download required", "instant experience", or wants to understand how App Clips appear in App Store search. For general App Store discoverability, see aso-audit. For marketing campaigns, see ua-campaign.
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。
When the user wants to improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.
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。
Diagnoses and reduces churn — cancellation flows, save offers, failed-payment recovery, at-risk detection, and the product and service causes underneath. Use this when churn is rising or unexplained, to design a cancellation or win-back flow, to recover involuntary churn, to identify at-risk accounts before they leave, or to decide whether a retention problem is a product 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。
Plans and delivers a single project — scope, estimation, scheduling, critical path, tracking, and recovering when it slips. Use this to plan a project, build or challenge a schedule, estimate credibly, track progress meaningfully, or recover a project that is late.
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。
Finds out whether a problem is real and a solution would work, before building it — recruiting the right people, interviewing without leading them, separating what users say from what they do, naming the riskiest assumption and testing that one first, and reaching a decision rather than a summary. Use this to validate a problem, test an idea cheaply, decide whether to build something, or fix a discovery process that keeps confirming what the team already believed.
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。
Universal UI/UX design principles covering visual hierarchy, interaction laws, typography foundations, and WCAG accessibility requirements. Use when making design decisions not covered by a specific design system, validating principle compliance, or resolving conflicts between aesthetics and accessibility. Design-system-agnostic and applies to every surface.
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。
Generates professional cover letters for journal submissions and job.
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 creating journal cover images, generating scientific artwork prompts, or designing graphical abstracts. Creates detailed prompts for AI image generators to produce publication-quality scientific visuals.
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。
Generates a journal-ready cover letter from manuscript metadata, highlights, and journal-fit notes. Use when preparing an academic submission package and you need editor-facing language that clearly states novelty, relevance, declarations, and corresponding-author details.
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。
Generates complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, reference literature pack, and self-critical risk 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。
Generates complete programmed-cell-death (PCD) / regulated-cell-death (RCD) bulk-transcriptome oncology research designs from a user-provided disease and mechanism theme. Always use this skill whenever a user wants to design, plan, or structure a cancer bioinformatics study built around cell-death patterns, tumor microenvironment, prognostic modeling, immune landscape analysis, mutation profiling, and computational drug sensitivity. Covers five study patterns (mechanism-gene-set, subtype-discovery, prognostic-signature, immune-response stratification, translational drug-hypothesis) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.
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。
Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Generates complete cross-disease shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Always use this skill whenever a user wants to design, plan, or build a multi-dataset study linking two related diseases through shared DEGs, enrichment, PPI hub genes, public validation, regulatory-network analysis, immune infiltration, drug-gene interaction screening, and optional qRT-PCR or cell-line validation. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, regulatory-network interpretation, immune/drug follow-up, bioinformatics-plus-validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.
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。
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Enforce XQUIC test coverage and local validation gates. Use when code, build, or test changes need verification; before commit or pull request; or when asked to build, test, validate, or verify XQUIC. Requires paired happy-path and abnormal-path unit tests, the complete local unit suite, and explicit endpoint coverage or gap reporting for production behavior 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。
Use Trivy vulnerability scanner in offline mode to discover security vulnerabilities in dependency files. This skill covers setting up offline scanning, executing Trivy against package lock files, and generating JSON vulnerability reports without requiring internet access.
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 and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async 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。
Discover public GitHub repositories that import defineTask from @a5c-ai/babysitter-sdk and maintain a deduplicated catalog of those repositories in docs/repo-with-babysitter-processes.md. Excludes any repo named "babysitter" (to filter out forks of this monorepo). Invoke when asked to find, discover, catalog, or refresh "repos using babysitter", "babysitter in the wild", or "who else is using babysitter".
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 a task needs an app on the user's own screen — a native desktop application (instrument control software, Origin, GraphPad, ImageJ, a reference manager, a spreadsheet), or a desktop browser window that needs window-level control. Read before the first `computer` call. Covers the observe-act-observe loop, why element indexes go stale, which action to prefer for which control, what "unverified" means and why it must never be reported as success, and the recovery for each error code. Not for page-level web automation (use the browser connector) and not for anything that can be done from the shell.
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。
Scaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.
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。
视频封面/缩略图设计。为口播视频、B站/视频号/小红书/YouTube 生成高点击封面，保持同一账号的「家族气质」（统一版式基因，但每条重新设计不套死模板）。当用户说「做封面」「视频缩略图」「首图」「B站封面」「小红书封面」「cover」「起个封面」时触发。输入是标题/主题/一张截图，输出对应平台比例的封面图。
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。
Searches the Azure skills catalog and recommends installable agent skills by matching an Azure task to skill metadata and plugin installation guidance. WHEN: before starting any task that involves an Azure or Microsoft-cloud service, product, or data source, when no currently loaded skill or tool already covers it.
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。