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概要と使いどころ
Create a lean, decision-ready Product Requirements Document with an 8-section template: summary, contacts, background, objective, market segments, value propositions (6-part JTBD template), solution, and release. Lean by default (about one to two pages), with a fuller spec on request. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
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Scan GDDs against the entity registry for cross-document conflicts. Grep-first approach targets conflicting sections, different stats.
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。
Legal document drafting -- contracts, memos, briefs, complaints, demand letters, opinions, discovery, settlements, ToS, privacy policies. Full pipeline: document structure, per-section writing, Bluebook citation, case law lookup (CourtListener API), regulation lookup (eCFR API), DOCX output, and TDD-style verification (defined terms, cross-references, placeholders, boilerplate, citation format). Triggers on: 'draft a contract', 'write a legal memo', 'create an NDA', 'write a brief', 'legal document about', 'draft a complaint', 'terms of service', 'privacy policy', 'demand letter', 'settlement agreement', 'legal opinion', 'discovery requests', any request to produce a legal or law-related document.
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Evaluates an ML GPU cluster for a cloud trial or acceptance test: environment dump, isolated newest PyTorch, matmul FLOPS (MAMF/MSMF) on every GPU while the others compute, intra-node all-reduce bandwidth and per-call latency of every collective, the same inter-node on every node you were given (omit those sections if there is only one node), fio on local disk and shared FS, dated markdown report. Use when the user asks to evaluate a cluster, kick the tires on trial nodes, run cluster acceptance, or measure GPU/network/storage. Canonical copy: https://github.com/stas00/ml-engineering/blob/master/skills/evaluate-cluster/SKILL.md
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。
Content-adaptive wisdom extraction that reads content first, detects which wisdom domains are present, and builds custom sections around them, with five depth levels and mandatory contrarian takes; pulls YouTube via fabric and articles via WebFetch. USE WHEN extract wisdom, analyze video, analyze podcast, extract insights, key takeaways, summarize interview, distill content. NOT FOR static Fabric extract_wisdom pattern (use Fabric).
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Owns the Ideal State Artifact — the primitive holding a project or task's articulated ideal state; scaffolds, interviews, grills a half-formed idea into shape via checkpointed discovery, scores completeness, reconciles feature excerpts to master, seeds from a repo, and appends decisions/changelog/verification across a locked seventeen-section order. USE WHEN ISA, ISC, ideal state, ideal state criteria, project specification, hill-climb, articulating done, fog, not yet specified, grill me, discovery interview. NOT FOR creating new skills (use CreateSkill).
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 competitive SEO content briefs with per-section word counts, competitor scoring, keyword density guidance, and page-type templates. Supports both new page briefs and improve-existing-page briefs. Use when user says "content brief", "write a brief", "content outline", "blog brief", "service page brief", "brief for", "writing brief", "content plan", or "outline for".
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。
Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.
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。
Fill in the per-paper TODO sections of research-wiki/papers/<slug>.md pages that literature-ingest skills leave as bare scaffolds. Use when user says 'enrich wiki', 'fill paper TODOs', 'wiki body 補完', '把 paper 摘要寫進 wiki', 'research-wiki 自動填', or after a batch ingest that left papers/ as TODO scaffolds.
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Search and progressively read open-access academic papers through DeepXiv. Use when the user wants layered paper access, section-level reading, trending papers, or DeepXiv-backed literature retrieval.
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Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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。
Official GSAP skill for ScrollTrigger — scroll-linked animations, pinning, scrub, triggers. Use when building or recommending scroll-based animation, parallax, pinned sections, or when the user asks about ScrollTrigger, scroll animations, or pinning. Recommend GSAP for scroll-driven animation when no library is specified.
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 a deterministic, read-only health check on an Obsidian wiki. Use for lint, vault health check, audit wiki health, find orphans, find dead links, frontmatter audit, provenance audit, or wiki audit. Reports graph, link, frontmatter, provenance-ledger, empty-section, and stale-index findings; it does not reason broadly or repair files.
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。
Canonical three-section structure for Linear and Superset tickets in this repo. Use when creating, drafting, or grooming a ticket.
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 publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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Builds and debugs Shopify themes (.liquid files, theme.json, sections), develops custom Shopify apps (shopify.app.toml, OAuth, webhooks), and implements Storefront API integrations for headless storefronts. Use when building or customizing Shopify themes, creating Hydrogen or custom React storefronts, developing Shopify apps, implementing checkout UI extensions or Shopify Functions, optimizing performance, or integrating third-party services. Invoke for Liquid templating, Storefront API, app development, checkout customization, Shopify Plus features, App Bridge, Polaris, or Shopify CLI workflows.
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Takes a structured list of changes from changelog-scan and produces a clean, categorized, user-facing release notes draft. Follows project voice and conventions. Never publishes — only proposes a draft file or section.
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House cold-start interview (request list + prior memo), or --new-deal for deal-specific context. Modular: identifies which practice areas apply (M&A, Board & Secretary, Public Company, Entity Management), then asks targeted questions for each active module and writes only the relevant sections to the plugin config. Use on fresh install, when CLAUDE.md still has [PLACEHOLDER] markers, when starting a new deal, or to re-check integrations or refresh a module.
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把已核验的权威指南按原文结构逐节展开讲解，并给出原文定位，帮医生读懂原文；也可把指南整理为学习路径图（学习顺序）。不得生成诊疗方案、床旁处置步骤、疾病 SOP、急诊行动卡、个体患者建议或药物剂量说明。
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。
Sunday-paper 风格, 大字 serif headline + 双栏正文 + 编号 sections
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 helm chart updates for Bifrost. Detects config.schema.json changes since the last helm release, applies user-requested changes, updates values.yaml / values.schema.json / _helpers.tpl, bumps Chart.yaml version, updates helm README (Latest Version + Upcoming section), creates a docs MDX changelog, and updates docs.json navigation. Ends with a diff review to verify every helm field maps back to a config.schema.json counterpart. Invoked with /helm-update.
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 structured PDF checklists and reports with tables, sections, and scoring criteria using Python 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。
Generate structured PDF documents with tables, sections, and scoring using Python libraries in execute_code_sandbox
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 structured medical SOAP notes with all required sections in a single comprehensive file write.
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。