2.5万 件 ・ 関連度順
AI 电影/短剧解说视频自动生成（AI 解说大师 CLI Skill）。当用户需要创建电影解说视频、短剧解说、影视二创、AI 配音旁白视频、film commentary、video narration、drama dubbing、movie narration 时触发。内置电影素材库、BGM、多语种配音、解说模板。通过 narrator-ai-cli 命令行实现：搜片→选模板→选 BGM→选配音→生成文案→合成视频的全流程自动化。CLI client for Narrator AI video narration API.
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Use when the user asks to "turn this idea into posts for every platform", "write the X thread / LinkedIn post / 小红书 note", or "spec the carousel slides"; turns one idea into N platform-native ready-to-paste packages — post/thread, caption, Threads text, 小红书 note, link post with first-comment placement — each adapted per the dated platform norm card (never verbatim cross-posting), every product claim held to approved claims-ledger wording or flagged [needs source], every variant tagged {formula | hook family | CTA type | signal optimized}, plus a carousel slide-spec mode (intro/content/outro slide roles, per-element char budgets, 小红书 3:4 / IG 4:5 / LinkedIn document PDF artboards, text-safe-zone + alt-text checklist). Not for creator deliverable briefs — use brief-generator; not for repurposing existing assets with paid boost — use content-amplifier. 社媒文案/一稿多发改写/小红书笔记/轮播图脚本
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Diagnose and manage an Algolia-backed search incident with evidence, containment, and reversible recovery. Use when users see failed, stale, slow, or irrelevant search results. Trigger with "Algolia incident", "search outage", or "Algolia degraded".
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Org-grade attack-surface mapping: given a company's legal identity, discover its ENTIRE owned internet footprint — corporate family -> owned domains -> owned netblocks/ASN -> live assets — with attribution discipline, not just DNS breadth. The org-first attribution pyramid (legal entity -> LEI/registration -> corporate family -> owned domains -> owned netblocks/ASN -> live assets). Corporate-identity resolution via the GLEIF LEI API (legal name -> LEI, exact-LEI direct-children expansion, downward-only depth-capped BFS, NEVER a name re-resolution), SEC-EDGAR full-text search + Exhibit-21 subsidiary entity names, OpenCorporates entity corroboration, Wikidata SPARQL corporate graph (P856/P355/P749/P1830). Domain attribution via reverse-WHOIS (WhoisXML preview-then-purchase quota guard, SecurityTrails associated-domains — both paid), crt.sh O= certificate-transparency organization pivot (keyless), infrastructure correlation (shared NS/MX/SaaS-TXT, netblock membership, reverse-DNS PTR), and the independent-evidence combiner (1-prod(1-w_i), rule of three, OwnerTier NONE/WEAK/MODERATE/STRONG/CONFIRMED) — discover-only `related:` candidates are NEVER auto-scanned. Netblock/ASN attribution via org-first RIR queries (ARIN Whois-RWS org-handle search, RIPE DB organisation + inverse-org search) that recover 'dark netblocks' with no DNS link to the seed, ASN discovery (RIPEstat + BGPView union), the HYPERSCALER-SCOPE GUARD (never attribute a whole AWS/GCP/Azure/Cloudflare announced range to a tenant — keep only the seed-containing block, tag shared_hosting_cdn), and org-identity-seeded internet-scan-index queries (Shodan/Censys/ZoomEye/FOFA/BinaryEdge org: filters + an always-on keyless crt.sh fallback). Promote-to-scan triage ranks forgotten discover-only netblocks by remote-access exposure (gateway-vendor/KEV/control-plane/datastore port scoring) into an operator queue. Attribution confidence rubric + anti-patterns (namesake grafting via GLEIF name re-resolution, single-signal ownership, hyperscaler over-attribution, privacy-WHOIS pivot poisoning, RIR org-name collisions). Passive/keyless-first OSINT only — every paid-key dependency is enrichment on top of a keyless core, never a hard requirement. Use when mapping an organization's full corporate-family internet footprint, resolving a legal entity to its LEI/subsidiaries, discovering domains/netblocks/ASNs an org owns beyond its one seed domain, auditing M&A/shadow-IT sprawl, or scoping an engagement that starts from a company NAME rather than a domain.
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Turn a topic or finished script into a complete, publish-ready explainer video — led by an AI presenter from an authorized adult presenter image, or performed with no presenter in one of twelve explainer styles (nine visual styles and three narrative ones). Not for promos, ads, or footage montages. Use for new presenter or styled explainer videos and for continuing, revising, captioning, lip-sync repairing, or re-exporting an existing job. Use it whenever the user asks for 数字人口播 / 数字人讲解, for a 讲解视频 / 动画讲解 / 科普视频, asks which styles or templates are available (有哪些风格 / 看看风格 / 风格模板), or names one of the styles: 留白, 信号/科技感, 手帐/手绘, 立体书/纸艺, 波普漫画, 一镜到底/3D, 图纸与注脚, 黑板报, 黏土小城, 推演/动态图解, 案卷/调查复盘, 小剧场/生活场景. Keep model and provider selection capability-based and record the actual choices per job.
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Build a Grida slides deck — a `.canvas` bundle in slides mode whose pages are SVG documents (16:9, one SVG per slide). Use when creating a presentation, pitch deck, slideshow, or talk.
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Make short product / skill motion videos (10–30 s, narrated feature demos up to ~60 s) the way high-end launch clips are made — kinetic type, real UI as the material, a hand that causes every reaction, beats that carry into each other instead of replacing each other, hard cuts only where they mean something — without HyperFrames or Remotion. A single HTML composition where every value is a pure function of time, built from a library of measured moves (springs, entrances, carries, contact, camera), rendered frame by frame with real shutter motion blur (1080p30 drafts, 4K60 final), with synthesised sound effects that share one room and an oracle that fails a slideshow before a human has to watch it. Use for 做个动效短片 / 产品宣传片 / 介绍视频 / 发布视频 / 15 秒 launch video / 给 skill 或 app 做个视频 / promo / teaser, for turning real pages or screen recordings into a cut, for product feature demos (产品功能演示 / 功能展示动效) where the UI is rebuilt in HTML from screenshots instead of screen-recorded (不录屏 / 复刻界面 / 截图还原 UI), for narrated cuts with voice-over and subtitles (配音 / 字幕 / 换配音) and for the same film in other languages (日语版 / 多语言 / 本地化), for adding motion vocabulary (动效库 / 转场 / 衔接), and for auditing why a motion video reads as a slideshow (像 PPT / 没节奏 / 一个一个展示 / 转场生硬).
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Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
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Audit, write, and improve developer documentation using Google's Developer Documentation Style Guide and Technical Writing courses. Use this skill for any documentation work, even when the user names no style guide: "audit our docs", "review this README", "write a README", "getting started guide", "how-to or tutorial", "API reference", "docstrings", "CLI help text", "changelog or release notes", "migration guide", or "our docs are confusing". Also use it when writing docs from code, rewriting a doc for clarity, fixing headings, procedures, or code samples, or enforcing consistent voice and terminology across a docs set — prefer it over editing documentation unaided. Covers reader and doc-type fit, second person and active voice, procedures, headings, lists and tables, code samples and placeholders, link text, notices, the word list, timeless docs, and accessibility. For code comments and naming, see clean-code. For marketing or landing-page copy, see storybrand-messaging.
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Plans and scripts short-form and long-form video, and designs the packaging — titles, thumbnails, and openings — that determines whether it gets watched. Use this to script a video, plan a series, fix retention or click-through problems, design thumbnail and title concepts, or turn written content into video. For a YouTube channel specifically — idea selection, retention teardowns, and channel-level strategy — use `youtube-producer`.
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Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.
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Identifies the real underlying study design used in a medical or biomedical paper, distinguishes primary and secondary design components when papers are hybrid, and converts the paper into an evidence-aware design label suitable for literature appraisal, evidence grading, and downstream review workflows. Always identify the actual design from what the study did, not from how the authors describe it. Never fabricate references, metadata, or study features.
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Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management. Masters incident command, blameless post-mortems, error budget management, and system reliability patterns. Handles critical outages, communication strategies, and continuous improvement. Use IMMEDIATELY for production incidents or SRE practices.
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Premise, theme, controlling idea, subject selection and logline for scripts (前提/主题/立意/选材/戏核) — merged from Lajos Egri's premise-as-tyrant and dialectic, Robert McKee's controlling idea (value + cause) and idea/counter-idea, Neill Hicks' five premise questions, Lisa Cron's "third rail" (desire vs misbelief, origin scene, what-if), Julian Hoxter's logline and theme-attitude rule, Blake Snyder's logline/genre/title tests, and Lu Jun's Chinese craft chapters on 选材要严/开掘要深/视角要新/戏核要好/情节要奇. Use when turning a vague idea into a premise, finding what a story is really about, choosing a subject and angle, testing a logline, deciding the ending from the theme, or diagnosing a script that "has events but no story".
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视频标题生成与落盘工作流。Use when the user asks to title a finished or planned video, especially 抖音/小红书视频标题, 视频标题.md, 成片同级目录, 对标标题共性, 两段式标题, 基于视频主题起标题, 洗稿后标题, or replacing generic dbs-xhs-title for video titles. It derives the real video theme first, studies benchmark title patterns when provided, generates 8-12 theme-first two-part candidates, recommends/selects one, and writes 视频标题.md next to the final MP4 when requested.
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End-to-end video production orchestration for the content-creation workspace. Use when the user asks to make, recreate, package, render, QC, or archive a video; when the user says 制作待制作队列, 按交接稿制作, 制作队列视频, or points at a handoff project folder under 01-内容生产/视频工作台/待制作/ (contract file 交接稿.md inside) or an image-card handoff under 01-内容生产/图文工作台/待制作/ (its frontmatter is the binding production contract); when a request could route to HyperFrames, Remotion, captions, TTS, download/transcription, website/product videos, motion graphics, or reference replication; or when the user needs production planning before editing.
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Motion-first AI video director for topic-to-video creation. Use when the user gives a theme, idea, article, script, or brief and wants AI to freely create a real animated video instead of a PPT/slideshow-style explainer; when deciding visual metaphors, motion grammar, scene beats, component choices, generated image needs, HyperFrames/Remotion implementation strategy, and anti-PPT QC.
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AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or decisions.
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This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.
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Use when asked to compare two annual reports, put two companies from the same sector side by side, or check whether two companies' adjusted numbers mean the same thing. Takes two uploaded annual reports from one sector and compares them on five ratios computed with identical definitions from audited figures, flagging every place the companies define a metric differently. Produces a page-referenced comparison table, a definitions-differ section (adjusted vs statutory, inside vs outside the audited statements), and three questions to ask before believing either company's highlights. Educational, not financial advice.
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Extract a brand's visual and verbal identity into an applicable guideline kit — tokens, voice rules, and do/don't pairs — then apply it consistently to any artifact. Use when asked to apply brand guidelines to a document/deck/page, to extract a brand kit from existing materials or a website, to keep AI-produced artifacts on-brand, or to write lightweight brand guidelines for a startup. Produces a compact brand kit (visual tokens + voice rules + application examples) and/or an artifact restyled to it. For a creator's personal voice use creator-brand-kit; for building new UI systems use frontend-design.
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Respond to a brand or executive impersonation incident — deepfaked executives, cloned support lines, fake apps, spoofed domains, or AI-generated scam content wearing your name. Use when a deepfake of a leader is circulating, customers report a fake version of your product or support channel, or to prepare the impersonation playbook before it happens. Produces an incident response: verification protocol, takedown sequencing by platform, customer and public communications, and the hardening plan. For general crisis comms use press-release/pm-crisis skills; for security incidents inside your systems use security-incident-response.
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Build the storyline and slide structure for a board presentation. Use when asked to create a board deck, board presentation narrative, board meeting slides, or quarterly board update. Produces a complete slide-by-slide structure with narrative beats, talking points, and slide content guidance.
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Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.
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