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概要と使いどころ
S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).
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。
Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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。
Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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。
Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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。
Apply the four published expert-consensus clinical scenarios to manage GI adverse events in complex GLP-1 receptor agonist patients — persistent nausea mid-titration, GERD/heartburn after up-titration, elderly with sarcopenia risk, and insulin-treated elderly with CKD plus diarrhoea. Use when a clinician describes a patient resembling one of these archetypes, or asks "how to manage GLP-1 in an elderly patient with CKD", "patient on liraglutide developed reflux", "sarcopenia worry on semaglutide in 80-year-old", or "persistent nausea on semaglutide despite escalation". Grounded in Gorgojo-Martínez 2023 Table 4.
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。
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
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。
Type-safe URL query state management with nuqs 2.x. Use when writing or reviewing code that uses useQueryState, useQueryStates, URL search params, query parameters, nuqs parsers, createLoader, createSearchParamsCache, createSerializer, or URL state in Next.js, React, Remix, React Router, or TanStack Router.
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。
Publishes HTML or Markdown as display.dev URLs with public, company, or private access. Supports signup, claiming, sharing, browsing, bounded reads, exact edits, and comment iteration. Use when the user asks to "publish this", "share this", "share with the org", "post this online", "make a private link", "share with [email]", "publish a report", "list my artifacts", "find an artifact", "publish a large file", "publish this generated HTML file", "read this artifact", "search inside this artifact", "change this passage", "make a copy of this artifact", "duplicate version", "create a display.dev account", "claim this publish", "add a company email domain", "transfer an email domain", "change the email domains add-on", "show my referral link", "show referral rewards", "watch comments on this artifact", "respond to comments", or "resolve this comment thread". Use whenever a request contains a display.dev artifact URL (`*.dsp.so/*`). Anonymous publishing returns a 30-day preview and browser claim URL.
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。
Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.
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。
Fornece orientação para treinamento RL de nível empresarial usando miles, um fork pronto para produção do slime. Use ao treinar grandes modelos MoE com FP8/INT4, necessitando alinhamento treino-inferência ou exigindo RL especulativo para máxima taxa de transferência.
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。
Fornece orientação para pós-treinamento de LLM com RL usando slime, um framework Megatron+SGLang. Use ao treinar modelos GLM, implementar fluxos de trabalho personalizados de geração de dados, ou precisar de integração estreita com Megatron-LM para escalabilidade em RL.
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。
Plataforma GPU serverless em nuvem para executar workloads de ML. Use quando você precisar de acesso GPU sob demanda sem gerenciamento de infraestrutura, fazendo deploy de modelos de ML como APIs, ou executando jobs em batch com auto-scaling.
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。
Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing 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。
Discover ordinary public webpages with LLM-optimized Tavily search results. Use when no specific URL is known and the user needs a quick public-web lookup, articles, recent news, sources, or domain-filtered results. This is the default Tavily leaf for simple public-web discovery. Do not use when the user already supplied the target URL (use tavily-extract), wants URLs under one known site (tavily-map), wants many pages' content (tavily-crawl), needs a full cited synthesis (tavily-research), or needs named platforms, login state, platform-native content, or cross-platform collection (Agent Reach).
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 clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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。
MUST USE when user wants to 调研/research/搜索/search/查/找/look up anything on the internet — e.g. 全网调研 X / 帮我调研一下 X / 查一下 X / 搜搜 X / 看看大家怎么评价 X / X 上有什么讨论 / research this topic。 Also MUST USE when user mentions any platform or shares any URL/链接: 小红书/xiaohongshu/xhs, Twitter/推特/X, B站/bilibili, Reddit, V2EX, LinkedIn/领英/招聘/求职/jobs, YouTube, 小宇宙播客, 雪球/股票行情, RSS feeds, or a non-GitHub web URL. Multi-platform, multi-backend routing (OpenCLI / per-platform CLIs / APIs). Zero config for 6 channels. Run `agent-reach doctor --json` to see which backend serves each platform right now. NOT for: GitHub 相关操作（包括 GitHub URL、代码或仓库搜索、Issue、PR、 Actions 和 Release），由 github-cli 处理；写报告/数据分析/翻译等内容加工 （本 skill 只负责从互联网获取内容）；发帖/评论/点赞等写操作；已有专门 skill 的平台（先用专门 skill）。 【路由方式】SKILL.md 包含路由表和常用命令，复杂场景需按需阅读对应分类的 references/*.md。 分类：search / social (小红书/推特/B站/V2EX/Reddit) / career(LinkedIn) / web(网页/文章/RSS) / video(YouTube/B站/播客)。
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。
Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.
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 list all URLs on a website, with optional search filtering. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", or "list all pages". Essential when the user knows which site but not which exact page.
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。
Things 3とClaude Codeのタスクを双方向で共有する。URLスキームによるタスク送信とAppleScriptによるタスク読み取りに対応する。macOS環境でThings 3とのタスク同期が必要な場合に使用する。Do NOT use for macOS以外の環境でのタスク管理。
Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)
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。
Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).
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。
Extrahiert aus einem Chatverlauf (aktuelle Session oder Transkript-Dateien) einen wiederverwendbaren Skill — oder verbessert einen sehr ähnlichen existierenden Skill, statt ein Duplikat zu erzeugen. Nutze diesen Skill bei „mach daraus einen Skill", „das sollten wir als Skill festhalten", „extrahiere Skills aus diesem/alten Chatverläufen", „diese Arbeitsweise wiederverwendbar machen", oder bei `/skill-extract`. Deckt auch Bulk-Läufe über viele alte Transkripte ab (mit Datenreduktion über Subagenten). Für wiederkehrende AUTOMATISIERUNGEN (Cron/Schedule/Loop) stattdessen den Schwester-Skill workflow-extract nutzen.
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 create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.
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。