동영상·이미지·음성 스킬

Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a ___ · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an Accept tap; a chime + soft per-swap ticks track the swaps. DETERMINISTIC assembly — an HTML card (real DOM text) rendered to PNG via headless Chrome, chroma-keyed, its magenta window refilled per-product in PIL, then animated + audio-synthed with FFmpeg. FREE (no paid model calls); the recipe supplies the brand line, product images, and payoff and gates the only optional paid step (a hero shot when the brand has NO usable photo → create-image-fal). Use for the airdrop-notification-carousel format.

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Assemble an animated character-explainer video ad (any art style — Pixar-style 3D, claymation, anime, paper-cut, felt) (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format.

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Generate an AI creator talking to camera, saying an approved script, as one continuous track — H3 Max reference-to-video through the GooseWorks fal-proxy (bills the Ads agent). Plans takes on line boundaries under H3's 15s cap, dry-runs a cost estimate, generates the first take alone so its voice can be locked and passed to every later take, joins takes with measured 0.10s dissolves, and moves each line's timing onto the words actually spoken. Use for any format with a generated creator speaking a script (split-screen, screen inserts, talking-head ads).

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Composite a generated creator track into a product layer, per beat — in the creator's zone on split beats (either side of a seam), full frame on creator beats, hidden on product beats — with the creator's voice as the audio (loudness -14 LUFS) and an optional ducked music bed, or a music-only or silent master for voiceless formats. Free, local ffmpeg. Pairs with footage-cutlist (the layer) and create-creator-takes-h3 (the creator). Use for split-screen (with an optional zoom and brand-colour seam bar), screen-insert and any creator-over-product format.

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Burned-in captions for a finished vertical video, three kinds. transcribe.py gets word timings from the video's own audio through the GooseWorks proxy (fal Whisper, bills the Ads agent, cents); captions.py burns one to three words at a time with Pillow + ffmpeg (no libass needed), either pinned to a split-screen seam (plate 25% above / 75% below) or at a fixed height, in a plate, outline or one-word serif style, with an optional red hook card; plates.py burns per-beat caption blocks (black, one union silhouette, placed in the emptiest band) for formats with no voice. The last caption (the CTA) holds to the final frame. Use as the last step of any video ad.

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Assemble an editorial-motion podcast-clip ad from a config — a real clipped podcast MP3 carries the narrative while N flat limited-palette editorial-illustration keyframes (one look pack) are animated NOT by generative i2v but by DETERMINISTIC ffmpeg ken-burns (zoompan) + hard cuts (no crossfades, which expose geometric drift), each beat snapped to its spoken line, the real audio muxed, Whisper-driven captions burned only mid-sentence, and closed on a PIL brand end card — never AI-rendered text. This is the FREE deterministic assembly stage (ffmpeg ken-burns + hard concat + audio mux + captions + end card); the real audio is clipped from source and the keyframes come from create-image-fal. Use for the editorial-motion-podcast format.

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Generate a single photoreal or designed image with OpenAI gpt-image via fal.ai. Supports gpt-image-1 (default, fixed sizes — the FAL fallback for Higgsfield's `gpt_image_2`) and gpt-image-2 (`openai/gpt-image-2`, custom output sizes up to 3840px). Routes to text-to-image or the edit variant depending on whether a reference image is provided. Use for photoreal character anchors, scene keyframes, and designed sheets (e.g. storyboards) where precise layout and legible text matter.

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Image-to-video (or text-to-video) via any FAL video model (Kling, Seedance, Veo), ROUTED THROUGH THE GooseWorks fal-proxy so the call bills the Ads agent. The template recipe names the model + params; image_url inputs must be public URLs (the orchestrator hosts local frames with the MCP media_upload). Returns the result video URL and downloads it. Use for the generative base clip of any video-ad format.

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Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration host character (who, where, tone, voice and music come from the recipe choices) walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.

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Assemble an iMessage notification-cascade video ad (≈14s, 9:16) from a plate of a phone lying face-up (setting chosen by the user) + 3–5 messages — authentic Apple Messages banners composited in PIL (SF Pro text, green Messages icon, warm translucent-greige fill, soft shadow) spring in one-by-one at the BOTTOM and push the stack UP, a right-aligned Show-less/X pill rides above, the X clears the stack, then a serif end card resolves. FREE assembly (PIL + ffmpeg); the recipe supplies the per-brand plate, notifications, and end-card config and gates the paid plate-clean/music calls to their own capabilities. Use for the imessage-notification-cascade format.

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Shared helper that routes ALL paid media generation (FAL image/video, ElevenLabs music) through the GooseWorks proxies so every call bills the Ads agent — never a provider SDK's default host. Host-swaps the FAL queue URLs, loads the agent token from the sandbox env (GW_MEDIA_PROXY_TOKEN) or ~/.gooseworks/credentials.json, and returns the result CDN URL. Every video-ad media capability imports this; templates never call a provider directly.

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Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image). Use for any task involving an image — questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots — and to re-check an image yourself when a description you were given lacks a detail.

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Monitor social media mentions, trends, and competitor activity for e-commerce brands. Set up listening workflows across Reddit, TikTok, Instagram, Twitter/X, and YouTube.

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Implement, review, or improve photo picking, camera capture, and media handling in iOS apps using PhotoKit and AVFoundation. Use when working with PhotosPicker, PHPickerViewController, camera capture sessions (AVCaptureSession), photo library access, image loading and display, video recording, or media permissions. Also use when selecting photos from the library, taking pictures, recording video, processing images, or handling photo/camera privacy permissions in Swift apps.

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Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, and async result streams on iOS 26+.

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Implement VoIP calling with CallKit and PushKit. Use when building incoming/outgoing call flows, registering for VoIP push notifications, configuring CXProvider and CXCallController, handling call actions, coordinating audio sessions, or creating Call Directory extensions for caller ID and call blocking.

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Support automatic audio switching for paired third-party Bluetooth headphones or earbuds with AudioAccessoryKit. Use when a companion app registers an audio accessory, an app extension reports worn/removed placement or connected source-device changes, or AccessoryControlDevice capabilities and errors need handling. Do not use for general AVAudioSession routing, Bluetooth transport, or initial accessory pairing.

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Control motorized camera docks and enable intelligent subject tracking using DockKit. Use when discovering DockKit-compatible accessories, implementing camera subject tracking for faces or bodies, controlling dock motors for pan and tilt, configuring framing behavior, setting regions of interest, or building video apps with automatic camera tracking.

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Build presentation plans for PPT / slides / decks through a conversation-first workflow, then propose multiple visual directions with preview images before writing deck specs. Use when the user asks to create a PPT, presentation, deck, 答辩稿, 路演 deck, 产品介绍 PPT, 汇报 PPT, or when the user has only a topic or rough materials and needs help clarifying structure, style, and page planning before generation.

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Use when implementing ANY computer vision feature — image analysis, pose detection, person segmentation, subject lifting, text recognition, barcode scanning.

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Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.

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知らない分野にキャッチアップするとき、汎用的な言葉から界隈の用語・中心の人・場（学会・雑誌・個人ブログ・動画・リポジトリ・一覧）を特定し、 引用・リンク・著者を芋づる式にたどって、目当ての原文（論文も個人の記事や動画の字幕も）を要約せずそのまま ~/research/{話題}/sources/ に保存する。 周ごとに map.md（用語・人・場・原文の一覧）を書き足し、要る主張の全部に原文が付くか飽和したら止める。 見張りのモードでは、map.md の場（Qiita・Zenn のタグ、note、YouTube、ブログ、arXiv）の新着を RSS で引いて原文の冒頭つきで並べる。 以下で使用:「芋づる式に調べて」「この分野をキャッチアップしたい」「界隈の用語と人から掘って」 「論文とか先行研究を漁って」「既存の技術を真似したいから元ネタを探して」「上手い人のやり方を集めて」「原文で落として」「/imozuru」 「〇〇って研究あるの？原典ほしい」「全然わからないから流れを掘って」「どういう界隈でどう呼ばれてるか知りたい」 「〇〇の新着見て」「今週の〇〇の記事まとめて」「キャッチアップ」「/imozuru watch」 "dig into this field", "find the canonical papers on X", "literature dive", "what's new in X this week"。 以下には使用しない: 答えが1つの事実の確認（年・有無だけ・仕様1つ → そのまま検索）／ 手元の PC の中の資料を探す／読んだ資料の要約や解説を書く。

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Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

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Guide authoring of Audio Toolbox plugins (audioPlugin, audioPluginSource) that pass validateAudioPlugin and generate deployable VST/AU code. Use when creating audio effect or generator plugins, writing classdef files inheriting from audioPlugin, or troubleshooting validateAudioPlugin failures.

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