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概要と使いどころ
SwiftUI's actual mental model — view identity, lifetime, and dependencies (the Demystify canon), state ownership decision rules, Observation's per-property tracking, body-performance discipline, and the main-actor concurrency contract. Use when state resets mysteriously, views re-render too often, animations glitch between branches, choosing @State vs @Bindable vs plain property, or debugging "why did body run."
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Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.
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 SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).
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。
Expert knowledge for Azure Artifact Signing development including decision making, security, configuration, and integrations & coding patterns. Use when handling cert lifecycles, RBAC for signers, DGSSv2 migration, diagnostic logs, or CI/CD signing integration, and other Azure Artifact Signing related development tasks.
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。
One lifecycle poll over everything vuln-scanner produces - PR and advisory status, PVR triage transitions, and pending-disclosure aging, with a stars-secured impact headline and one action queue.
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。
5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates
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。
The weekly keep/cancel/promote/buy loop for a cold email sending fleet. Decides which domains are burned and should be cancelled, which warmed reserves to promote into campaigns, which capacity to demote, and how many new domains to buy — using measured reply, bounce, age and send-volume floors instead of gut feel. Use when asked "which inboxes should I cancel", "what should I replace this week", "my fleet costs too much", "I'm under my send goal", or as the Friday hygiene routine.
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。
Premium A+ and Brand Story — module design, lifestyle imagery, comparison charts, mobile 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。
Amazon product listing image strategy and optimization. Comprehensive shot planning, infographic design, lifestyle photography, mobile optimization, and conversion-focused visual content. Use when the user asks about Amazon images, product photography, visual optimization, or listing conversion.
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 shoot-ready Amazon product photography, listing image sets, infographics, lifestyle scenes, and production briefs. Use when a seller asks for an Amazon photo shot list, creative direction, image-pack plan, photography budget, DIY-versus-done-for-you decision, or handoff to a photographer or image-generation workflow. Do not use for listing copy or image-only performance auditing.
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。
Manage capa CLI configuration — `capabilities.yaml` / `capabilities.json`, skills, MCP servers, tools, hooks, sub-agents, rules, plugins, AGENTS.md / CLAUDE.md, security options, tool exposure modes, `capa wrap` shadow workspaces, `--passthrough` native writes, registries (including Claude marketplaces), activity traces, and the local Web UI. Use whenever the user edits the capabilities file, runs any `capa` command (init, install, add, wrap, clean, sh, start/stop/restart/status, auth, upgrade, cache, registry), wires up an MCP server, adds a skill from GitHub / GitLab / a registry / a remote URL / a local path, configures secrets via `${VarName}` placeholders, installs lifecycle hooks, defines sub-agents, or troubleshoots a failed install. Use even when the user only names a fragment ("add a skill", "wire up brave search", "wrap cursor", "block this phrase in skills") without saying "capa" — if a `capabilities.yaml` or `capabilities.json` lives at the project root, this is almost certainly the right skill. If the project does NOT yet have a capabilities file, point at the `bootstrap` skill instead.
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。
Rank and summarize the user's own WeChat Moments (朋友圈) feed so real life events and genuine information rise above promotion and small talk, weighted by how much the user actually talks to each author. Use when the user says 'summarize my Moments', 'read my 朋友圈', 'what did I miss on Moments', 'rank my Moments', or 'check my WeChat Moments'.
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。
Status match and status challenge rules for airlines and hotels. Covers free direct matches, paid concierge via statusmatch.com with fees, card-granted status, and critical lifetime/once-per-N-years restrictions that make wasted matches expensive.
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, triage, and write academic rebuttals and review responses. Use when an AI research agent needs to handle conference, journal, workshop, or grant reviews; classify reviewer concerns; decide what evidence or experiments can change the decision; write an AC-facing executive summary; draft per-reviewer responses; correct factual misunderstandings without sounding hostile; handle novelty, baseline, method, experiment, clarity, and scope objections; or produce a final rebuttal checklist.
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。
Review and revise academic papers with a senior-reviewer workflow. Use when an AI research/coding agent needs to work on an existing paper draft, PDF, LaTeX project, Word document, markdown manuscript, rejected paper, or paper idea that already has a concrete direction, and produce: paper maps, weakness audits, insight/novelty checks, reviewer objections, claim-experiment matrices, theory/proof checks, experiment plans, statistics/reproducibility audits, figure/table audits, ethics/compliance checks, revision plans, detailed section edits, or final submission checklists.
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。
Upload and fully manage YouTube videos and live events: metadata, thumbnails, captions, playlists, scheduled broadcasts, encoder streams, binding, lifecycle transitions, and cleanup.
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。
帮助用户从NLP理解层次的顶层重新规划人生；当用户感到迷茫、深陷日复一日的执行循环、不知如何突破现状时使用
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。
覆盖批量任务完整生命周期：参数叉乘创建、猴子补丁钩子追踪队列提交与执行、状态聚合状态机、队列中断取消、文件清理删除。 导航时机：理解批量任务从创建到完成的全流程、调试端到端任务生命周期问题、修改任务状态转换、排查任务卡住、新增生命周期阶段或状态。 排除：结果处理与输出媒体处理（见 ../result-processing/）、资源上传管理、前端UI逻辑。 关键词: 批量任务生命周期, createBatchTask, cancelTask, deleteTask, post_prompt, task_start, task_done, BatchToolsHandler, SubTaskStatus, CommonTaskStatus, prompt_queue, 猴子补丁, 参数叉乘, 状态聚合, 队列执行, 文件清理, SQLite WAL。
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 quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell 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。
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my 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。
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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 clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
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。