'reference' 검색 결과

Validate bibliography entries against citations in all source files. Find missing entries and unused references. Make sure to use this skill whenever the user has any concern about bibliography completeness or citation keys. Triggers include: "validate my bib", "check my citations", "find missing references", "I'm getting undefined citation errors", "are all my citations in the bib file", "check for unused references", "my bibliography is broken", "missing bib entries", "citation not found", or after adding new references from a lit review or before submission.

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Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable reference materials. Use PROACTIVELY for API docs, configuration references, or complete technical specifications.

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Use when generating video with Vidu models on Scenario via MCP: text-to-video, image-to-video from a single still, start and end frame interpolation, reference-to-video keeping characters and props consistent from up to 7 reference images or reference videos, anime-style motion, movement amplitude control, or deciding between Q3, Q2, Q1, and 2.0 tiers. Keywords: Vidu Q3 Pro, Q2 Turbo, Shengshu Technology, T2V, I2V, R2V, Reference2V, start end frame, background music toggle.

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Use when one look must hold across Scenario generations: one character across scenes, a turnaround, or a video animated from its references, one product across angles, one style across icon sets, a character from an uploaded drawing, a variant off an approved baseline, or deciding when references stop scaling and a trained model is due. Triggers: make this match, same character, on-model, reference to video, style reference. Keywords: consistency, identity, control map, seed, LoRA.

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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.

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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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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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🇺🇸 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"

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🇺🇸 SEO & GEO Dual-Engine Playbook 2026 — Rank on Google AND get cited by AI search (ChatGPT, Perplexity, Claude, Gemini). Battle-tested from AFFiNE (60k GitHub stars in 24 months) and 150+ AI startup consultations. E-E-A-T writing voice system, keyword funnel strategy, JSON-LD schema templates, comparison page SOP, IndexNow setup, Generative Engine Optimization content patterns. **Optimized for dev tools / OSS / B2B SaaS. 2C products (education, apps, games): see 2c-adaptation guide in references/.** 🇨🇳 SEO & GEO 双引擎增长手册 —— 同时拿下 Google 排名和 AI 搜索引用（ChatGPT、Perplexity、Claude、文心一言）的完整方法论。AFFiNE 60k stars 与 150+ AI 创业公司咨询实战验证。E-E-A-T 写作声音系统、关键词漏斗、JSON-LD 结构化数据、竞品对比页 SOP、IndexNow 推送、生成式引擎优化内容模式。**默认适用于开发者工具 / 开源项目 / B2B SaaS，2C 产品（教育/应用/游戏）请参考 references/2c-adaptation.md。** 🇯🇵 SEO & GEO デュアルエンジングロースプレイブック — Google検索とAI検索（ChatGPT、Perplexity、Claude）の両方で評価される手法。AFFiNE 60k starsと150社以上のAIスタートアップコンサルティングで検証。E-E-A-Tライティング、キーワードファネル、Schema.org構造化データ、比較ページSOP。**デフォルトは開発者ツール／OSS／B2B SaaS向け。2C製品（教育・アプリ・ゲーム）はreferences/2c-adaptation.mdを参照。** 🇰🇷 SEO & GEO 듀얼 엔진 성장 플레이북 — 구글 검색과 AI 검색(ChatGPT, Perplexity, Claude) 양쪽 모두에서 인용되는 방법론. AFFiNE 60k 스타와 150+ AI 스타트업 컨설팅으로 검증. E-E-A-T 글쓰기, 키워드 퍼널, Schema.org 구조화 데이터, 비교 페이지 SOP. **기본값은 개발자 도구/OSS/B2B SaaS용. 2C 제품(교육·앱·게임)은 references/2c-adaptation.md 참조.** Triggers: "SEO" | "GEO" | "Generative Engine Optimization" | "AI search optimization" | "ChatGPT SEO" | "Perplexity SEO" | "Claude SEO" | "search optimization" | "content SEO" | "technical SEO" | "schema markup" | "JSON-LD" | "structured data" | "IndexNow" | "E-E-A-T" | "keyword strategy" | "keyword funnel" | "comparison page" | "programmatic SEO" | "SaaS SEO" | "搜索优化" | "AI 搜索" | "内容 SEO" | "GEO 优化" | "生成式引擎优化"

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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.

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ALIGN — Agentic Loop Image GeneratioN. Paint a subject in the craft of a reference artwork as executable p5.js, Canvas, SVG or another mark-based medium, replay that craft's real order of work, and improve it through pixel-only cross-model review. The reference fixes the craft; the subject may be the reference's own scene (千里江山图 → p5.js) or another (清明上河图 → 武汉). Not for one-shot raster image generation.

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ALIGN — Agentic Loop Image GeneratioN. Paint a subject in the craft of a reference artwork as executable p5.js, Canvas, SVG or another mark-based medium, replay that craft's real order of work, and improve it through pixel-only independent Codex subagent review. The reference fixes the craft; the subject may be the reference's own scene (千里江山图 → p5.js) or another (清明上河图 → 武汉). Not for one-shot raster image generation.

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Modern C# idioms for any .NET project (C# 10+, .NET 6+): nullable reference types, records, readonly structs, primary constructors, pattern matching, async/await with CancellationToken, IDisposable/IAsyncDisposable, file-scoped namespaces, var usage, naming conventions (PascalCase members, _camelCase fields, I-prefixed interfaces), and class design rules. Apply whenever the project is a .NET 6+ project. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write self-documenting immutable value types with records and readonly structs. - Handle nullable reference types explicitly to eliminate NullReferenceException at compile time. - Implement async/await correctly with CancellationToken propagation and ConfigureAwait(false) in libraries. - Dispose unmanaged resources correctly via IDisposable / IAsyncDisposable and using statements. - Apply C# pattern matching (switch expressions, property patterns, list patterns) for cleaner branching logic. Do NOT use this skill for: - Framework-specific idioms (ASP.NET Core controllers, minimal APIs, EF Core — those live in aspnet-core-plugin skills). - Build tooling (dotnet CLI, NuGet, csproj) — see csharp-foundation:dotnet-tooling. - Testing patterns — see csharp-foundation:dotnet-testing.

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Rebalances context across Memory, Custom Instructions, knowledge files, and User Preferences in Claude Projects. Audits Memory for redundancy, staleness, and misplacement; prescribes optimization including Codification of stable Memory patterns into explicit User Preferences or Project CI rules. Use when user says "optimize my memory," "what should be in my memory," "trim my knowledge files," "reduce my context usage," "rebalance my project," "what should be in my preferences vs memory," or asks whether something belongs in Memory, a knowledge file, or User Preferences. Also trigger on symptom-phrased: "my project feels bloated," "Claude keeps forgetting things," "my context window keeps hitting limits." Activate whenever Memory-layer balance is the primary concern. Do NOT use for full Project audits (use rootnode-project-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or global-layer audits that don't touch Project Memory (use rootnode-global-audit if available).

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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Systematically add a new memory-system reference to Auto_Memory's reference hub. Use when analyzing a new agent memory system, adding reference/<system>/details.md, saving prompts, or updating reference/hub/technique_map.json.

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Unity Prefab / Scene / Asset の壊れた参照・Variant override・配線ミスを構造化応答で診断し、 dry-run → confirm 経路で安全に修復するための MCP ツールリファレンス。 Editor Bridge のセットアップ手順もここに含む。 トリガー: .prefab, .unity, .asset, .mat, Prefab, Variant, 配線, 参照, 壊れた参照, broken reference, GUID, fileID, override, パッチ, patch plan, validate refs, validate structure, where-used, inspect variant, inspect wiring, inspect hierarchy, wiring, null参照, null reference, MonoBehaviour, 階層, hierarchy, Transform整合, 孤立, orphan, 重複fileID, duplicate fileID, Editor Bridge, Bridge セットアップ, bridge setup, deploy_bridge, UNITYTOOL_BRIDGE_WATCH_DIR, watch ディレクトリ のいずれかに該当する作業で使用する。

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SharedPreferences (v2.5.5) for Flutter providing simple persistent key-value storage for non-sensitive user settings and app preferences. Use this skill when storing user preferences (theme mode, language selection, font size), app settings and configuration, feature flags or A/B test assignments, simple cache data, onboarding completion status, last selected values (filters, sort options), user consent flags, or any non-sensitive data that survives app restarts. Supports String, int, double, bool, and StringList types. NOT suitable for storing tokens, passwords, API keys, or any sensitive/encrypted data (use flutter_secure_storage instead). Ideal for quick persistent storage without database overhead.

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Build a reference system that returns the right reference in under 60 seconds. Use when setting up a design reference library, reorganizing existing collections, or helping teams build shared reference systems.

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Select, document, download, and checksum-verify the most appropriate NCBI reference genome on an HPC cluster. Use whenever an analysis needs a reference genome, genome FASTA, genome annotation, assembly accession, or genome index and no exact approved assembly is already available; when a user asks to fetch a species genome from NCBI; or when a pipeline must resolve and cache a reproducible reference assembly on a remote cluster.

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Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

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Analyze a set of design samples (HTML mockups + website URLs) and produce a structured style guide that anchors the rest of the stardust redesign pipeline. Use when the user supplies reference designs ("design samples", "mockups", "inspiration sites", "style references") and wants to extract a consistent visual language — type system, color palette, spacing, motion, and reusable component patterns — before redesigning a target site. Triggers on phrases like "distill these samples", "extract a style guide from these examples", "use these as design references", or "analyze these mockups". Outputs a `trait-matrix.json` (structured per-sample + cross-sample design tokens) and a `SAMPLES.md` narrative brief that `stardust:direct` reads under Mode B (anchor-reference precedence) when picking a redesign direction.

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Select, document, download, and checksum-verify the most appropriate NCBI reference genome on an HPC cluster. Use whenever an analysis needs a reference genome, genome FASTA, genome annotation, assembly accession, or genome index and no exact approved assembly is already available; when a user asks to fetch a species genome from NCBI; or when a pipeline must resolve and cache a reproducible reference assembly on a remote cluster.

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Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable reference materials. Use PROACTIVELY for API docs, configuration references, or complete technical specifications.

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