「ref」の検索結果

Add, review, or fix JavaScript interop in Blazor components. USE FOR: calling JavaScript from Blazor, calling .NET from JavaScript, collocated .razor.js modules, IJSRuntime, IJSObjectReference lifecycle, DotNetObjectReference, ElementReference, timing rules for when JS is available, IAsyncDisposable disposal of JS references, server-side JS interop safety. DO NOT USE FOR: general Blazor component authoring without JS interop needs (use author-component), forms (use collect-user-input).

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Use when finding papers or building a reference list. Searches PubMed, Semantic Scholar and bioRxiv/medRxiv, includes only references verified through an API, and generates BibTeX. Auditing an existing reference list is /verify-refs.

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精细化 AI 短剧 IP 创作技能（v0.6.0）。三阶段架构：Phase 1 创作（剧本+ref图，反复迭代）→ Phase 1.5 分镜图（每 grid 1-4 张候选静态图 = 视频首帧，工业级核心层）→ Phase 2 出片（按集解锁，4 模自动选）。v0.6.0 关键升级：① ref 库工艺偏置铁律（现代摩天楼易拟物，古建筑必出 chibi 人体，前期 IP 设计阶段就要避坑）② 即梦 5.0 失败模式 + 敏感词清单（3 类 fail 区分 / prompt 1500 字硬上限 / 反派词替换表 / 暧昧词清单）③ 分镜图 8 段 prompt 模板（CHARACTER/BACKGROUND/ACTION/SCENE/CAMERA/LIGHT/TEXT/STYLE）+ NOT humans 子句必加。沿用 v0.3.0 升级：Phase 1.5 分镜图层、4 模视频、ref 5-8 最优。v0.2.0：bash → Python subprocess、36 grid × 4-10s 变奏、红果必爆 7 招、工业级 ref 库 80-150 张、单 prompt 300-500 字。务必触发：用户提到短剧、微短剧、竖屏剧、AI 短剧、AI 漫剧、剧本创作、分镜、即梦/Seedance 出片、红果/番茄/抖音 IP 改编、爽剧、重生、穿越、赘婿、追妻、神医相师、AI 漫剧奇观、或"帮我做一部短剧"类请求。

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Update Han plugin documentation so every skill, agent, guidance doc, index, and cross-reference is current and accurate. On a non-default branch, scopes the pass to entities the branch actually touched. On the default branch, performs a full documentation sweep across the whole plugin. Use when updating, refreshing, syncing, auditing, or verifying Han's docs after changing skills, agents, references, or top-level guidance — including "update the docs", "doc sweep", "refresh documentation", "audit the docs", "make sure the docs are current". This is a repository-maintenance skill for the Han repo itself, not a general documentation skill — use /project-documentation to document features in arbitrary projects, /han-release to cut a release (and update CHANGELOG), and /update-pr-description for PR bodies.

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Intelligent code refactoring using IDE-level tools (rename, find-references, go-to-definition), AST-aware pattern matching, and TDD verification. Use for safe, large-scale refactoring with precision.

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Use when the user wants to simplify, clean up, refactor, tidy, or refine code — their staged/unstaged git changes or a target file/path. Reduces complexity, flattens nesting, removes redundancy and dead code, scores each change by value (holding low-value churn), then runs tests to prove nothing broke. Invoke on: "simplify", "simplify this", "simplify my code/changes", "clean up", "clean this up", "clean up my changes", "refactor this", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", "make it more readable", "polish before commit", or "absolute simplify". Acts on your working diff; for repo-wide dead code use absolute-prune; for lint/type debt use absolute-debt.

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Build customer case studies as marketing and chasm-crossing reference assets during PRD v1.0 Market Adoption. Triggers on requests to build case studies, produce customer stories, create reference content, or when user asks "build a case study", "customer story", "reference account", "case study interview", "before/after story", "social proof page", "logo wall". Outputs CFD-CASE-* evidence entries, GTM-CASE-* marketing assets, and updates ADO-REF-* with story status.

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Citation-integrity forensics: is every reference real, correctly attributed, and used in a context the cited work actually supports? Catches hallucinated references (no paper at the claimed arXiv id/DOI/venue, fabricated authors/year), metadata drift (wrong year/venue/version), and wrong-context citations (a real paper cited for a claim it never makes — or argues against). A hot zone for machine-generated papers. Decidable at L0 (text + canonical sources). Span-anchored to the evidence ledger (claims.json); the executor gathers canonical facts (DBLP / arXiv / DOI), then one FRESH cross-model thread per cited key proposes findings; reviewer != adjudicator. Emits citation-forensics.findings.json; NEVER computes the verdict. Triggers: "citation forensics", "check the references", "hallucinated citations", "wrong-context citation", "verify references", "引用核对".

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调香师与香水 (调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、accord 和弦、配方 (酒精中 % 浓度)、浓度分级 (parfum/EDP/EDT/EDC)、调香师的「香风琴」orgue、trials/mods 试样迭代、weighing 称量精度; (f) 法规与安全 — IFRA standards (原料限用)、EU 致敏原标签 (26→80+ allergens)、REACH、橡苔/atranol 限用、经典香水 (Mitsouko/Chanel No.5) 因法规被迫重配 (reformulation) 与创作自由之张力; (g) 评价/批评 — Luca Turin & Tania Sanchez (《Perfumes: The Guide》《The Secret of Scent》气味的科学)、香评人、Fragrantica/Basenotes 社区、vintage vs reformulation 之争、niche vs designer 之争。学派分歧: 法系天然传统派 vs 分子合成派、天然纯粹派 vs 合成实用派、haute parfumerie/niche 艺术派 vs mass designer 商业派、「鼻子=艺术家」vs「调香师=香气工程师/技师」、嗅觉科学之争 (Luca Turin 振动理论 vs 主流形状/受体理论)。不含: 香薰蜡烛/家居香氛纯生产营销、芳香疗法健康宣称 (aromatherapy)、纯化妆品配方学 (超出香精范畴)、DIY 家用香氛套件、精油传销/MLM。) Master OS — automated mastery of 调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、accord 和弦、配方 (酒精中 % 浓度)、浓度分级 (parfum/EDP/EDT/EDC)、调香师的「香风琴」orgue、trials/mods 试样迭代、weighing 称量精度; (f) 法规与安全 — IFRA standards (原料限用)、EU 致敏原标签 (26→80+ allergens)、REACH、橡苔/atranol 限用、经典香水 (Mitsouko/Chanel No.5) 因法规被迫重配 (reformulation) 与创作自由之张力; (g) 评价/批评 — Luca Turin & Tania Sanchez (《Perfumes: The Guide》《The Secret of Scent》气味的科学)、香评人、Fragrantica/Basenotes 社区、vintage vs reformulation 之争、niche vs designer 之争。学派分歧: 法系天然传统派 vs 分子合成派、天然纯粹派 vs 合成实用派、haute parfumerie/niche 艺术派 vs mass designer 商业派、「鼻子=艺术家」vs「调香师=香气工程师/技师」、嗅觉科学之争 (Luca Turin 振动理论 vs 主流形状/受体理论)。不含: 香薰蜡烛/家居香氛纯生产营销、芳香疗法健康宣称 (aromatherapy)、纯化妆品配方学 (超出香精范畴)、DIY 家用香氛套件、精油传销/MLM。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 调香师 / 香水 (Perfumery & Fragrance) — 香水创作与香气调配的职业认知操作系统，从业者/调香师/评香师/学习者视角。覆盖: (a) 第一性张力 — codifiable 理论层 (原料知识/香料化学/嗅觉家族分类/accord 和弦/配方结构 %/挥发度金字塔/IFRA 法规) ⇄ tacit 校准层 (嗅觉记忆/闻香训练/「the nose」/创作直觉/盲嗅识别)，这行的核心矛盾是「可背诵的原料与公式」与「不可言传的嗅觉记忆与品味」之间，嗅觉记忆是极致 tacit 技能; (b) 法系传统 vs 分子/合成派 — 法国 Grasse 天然香料 + haute parfumerie 艺术传统 (天然精油/原精/学徒制) vs 现代分子派 (aroma chemicals 香基/captives 专利分子/headspace 顶空捕香/功能香精)，两套对「香水是什么」的世界观; (c) 培养/传承 — ISIPCA (凡尔赛香水学校) + 四大香精公司内部调香学校 (Givaudan/Firmenich(DSM-Firmenich)/IFF/Symrise) + 学徒制 + 「鼻子」tacit 校准; 角色分工: perfumer(parfumeur 调香师) vs evaluator(评香师) vs flavorist(食用香精师); (d) 行业结构 — 四大香精公司寡头 (Givaudan/DSM-Firmenich/IFF/Symrise) + Mane/Robertet/Takasago，为品牌做 B2B 命题创作 (the brief → creation → mods 模型); vs niche/indie 小众独立香水 (artisanal/直营); 天然 (Grasse/精油) vs 合成 (香基/captives) 的成本与可持续之争; (e) 创作层 — 香调金字塔 (top/heart/base notes 前中后调)、ac

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Use when the user asks to batch-download academic PDFs with ref-downloader — either ALL references of one paper (Mode A: DOI or PDF input), OR a custom batch of papers (Mode B: DOI/title/arXiv-PMID list, or abstract query like "Author X's recent papers"). Not for one-off PDFs, paper search, or Zotero import.

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Hreflang and international SEO audit, validation, and generation. Detects common mistakes, validates language/region codes, and generates correct hreflang implementations. Use when user says "hreflang", "i18n SEO", "international SEO", "multi-language", "multi-region", or "language tags".

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Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the official @ahrefs/mcp server. Pairs with seo-backlinks for multi-source confidence weighting.

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Suede-owned referral and affiliate program discipline. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. NOT FOR: executing payouts or changing billing, launch-wide packaging (use suede-launch-packaging), lifecycle messaging (use suede-emails), or reporting unverified referral lift.

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When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' or 'partner program.' This skill covers program design, incentive structure, and growth optimization.

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Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.

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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.

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🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization. A full-cycle playbook covering Phase 0-5 (market validation, positioning, first 100 users, user interviews, beta-to-growth) plus open-source launch, Product Hunt, Reddit, SEO/GEO, conversion, and org principles. **Defaults to dev tools / OSS / B2B SaaS; for 2C products (education, apps, games) the launch channels & metrics differ — see gingiris-seo-geo/references/2c-adaptation.md.** 🇨🇳 AI 产品 / SaaS 企业出海完整 SOP — 从竞品调研到 Launch 到商业化的全流程操作手册。覆盖 Phase 0-5（市场验证、定位、前100用户、用户访谈、Beta转增长）+ 开源发布、Product Hunt、Reddit、SEO/GEO、转化与组织原则。**默认面向开发者工具/开源/B2B SaaS；2C 产品（教育/应用/游戏）的启动渠道与指标不同，见 gingiris-seo-geo/references/2c-adaptation.md。** 🇯🇵 AI製品/SaaS海外展開フルサイクルSOP — 競合調査からローンチ、マネタイズまで。Phase 0-5（市場検証、ポジショニング、最初の100ユーザー、ユーザーインタビュー、ベータから成長）＋オープンソース、Product Hunt、Reddit、SEO/GEO、コンバージョン、組織原則。 🇰🇷 AI 제품/SaaS 글로벌 진출 완전 SOP — 경쟁사 조사부터 런칭, 수익화까지 전 주기 플레이북. Phase 0-5(시장 검증, 포지셔닝, 첫 100명 사용자, 사용자 인터뷰, 베타→성장) + 오픈소스 런칭, Product Hunt, Reddit, SEO/GEO, 전환, 조직 원칙. Triggers: "go global" | "出海" | "overseas expansion" | "GTM" | "cold start" | "launch strategy" | "international expansion" | "海外增长" | "出海SOP" | "product launch overseas" | "海外进出" | "グローバル展開" | "글로벌 진출" | "go-to-market" | "出海打法"

🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified backlog to 10-day sprint cadence to veto power rules. What's inside: • Dual-layer Kanban system (master backlog + sprint board with unified tagging) • 10-day sprint standard process (Day 1 dev → Day 6 testable build → Day 10 ship) • Issue template with reproducibility requirements (3x reported = auto-severe) • Tri-party alignment meetings (daily standup / sprint planning / sprint review) • Operations veto power on releases (P0 bug = block shipping) • User feedback → product iteration closed loop (beta testing + interview SOP) • Core metrics framework (acquisition → activation → retention → monetization → referral) • Technical debt management (20-30% sprint capacity reserved) • Ready-to-use templates: Bug Report, Sprint Planning, Responsibility Matrix Built from: Real product strategy meetings + beta testing frameworks. References Supabase sprint model, Manus/DeepSeek commercialization alignment. By @WeiYipei. 🇨🇳 你的研发团队在做没人要的新功能，用户反馈的 Bug 堆了三个月没人动。运营觉得研发不听用户，研发觉得运营不懂技术。这份 SOP 给你从统一看板到 10 天迭代节奏到一票否决权的完整产研运协同框架。 🇯🇵 開発チームは誰も求めていない機能を作り、ユーザーから報告されたバグは何ヶ月も放置。このSOPは、統一バックログから10日スプリント、リリース拒否権まで、プロダクト×エンジニアリング×オペレーションの完全な連携フレームワークを提供します。 🇰🇷 개발팀은 아무도 요청하지 않은 기능을 만들고, 사용자가 보고한 버그는 몇 달째 방치됩니다. 이 SOP는 통합 백로그부터 10일 스프린트, 릴리스 거부권까지 제품×개발×운영 완전 협업 프레임워크를 제공합니다. Triggers: "product ops" | "engineering operations" | "product development SOP" | "sprint planning" | "iteration management" | "cross-functional alignment" | "product engineering ops" | "dev ops collaboration" | "产研运协同" | "迭代管理" | "产品研发运营" | "プロダクト開発運営" | "제품개발운영"

Wireframe a screen — text/ASCII by default, or hand-drawn HTML when the user says "sketch", "hand-drawn", "lo-fi HTML", "whiteboard", "graph paper", or "visual wireframe". Text mode produces a buildable ASCII spec Form and Prism can act on. HTML mode produces a single self-contained file with graph-paper background, marker headlines, sticky-note annotations, and hatched chart placeholders — looks like a designer's whiteboard, commits to nothing.

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Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.

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Start a complete plugin refactoring workflow that analyzes plugin structure, creates a refactoring plan with tasks, and guides through execution. Use when you need to refactor an entire plugin — triggers assessment, design, planning, and parallel agent execution phases.

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Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

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Store instructional prose once and reference it from multiple skills or agents instead of duplicating it. Use when the same steps, rules, or reference material appear in two or more SKILL.md or agent files in one plugin, when deciding whether a shared doc belongs in the plugin-root docs directory or a single skill's references directory, or when tempted to use a symlink or a ../ path to share a file between components.

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, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' or 'partner program.' This skill covers program design, incentive structure, and growth 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。