Evaluate a website, landing page, content, or any online asset through the eyes of pre-built synthetic ICP personas. Loads personas from buyer-persona-generator output, then runs them against target URLs. Supports three modes: structured scorecard, freeform focus group, and head-to-head competitive comparison. Reusable — run against the same site after changes, or against new content anytime.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Take someone from a business goal to a verified, paused-then-live Meta/Facebook/Instagram campaign without making them learn Meta's hierarchy. Use automatically for requests to launch, run, create, publish, set up, or plan Meta ads; use reporting skills for analysis-only requests.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on the word they belong to. The layout is SOLVED from the beats, never hand-placed. FREE assembly (Pillow plus ffmpeg) apart from one board photo and one drawing per subject, both one-time per brand. Use for the whiteboard format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Full video production sequence with script review, actual ingredient choices, controlled generation, editing, evidence-based quality review, polish, captions and delivery. A host binding supplies project storage, authentic approvals, provider access and billing.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video — birefnet cutout, then a cold-open text card + product carousel + annotated specimen-sheet end card, plus loudnorm + separate-pass music mux. FREE assembly (PIL + rsvg + FFmpeg); the recipe supplies the config and gates the paid BG/cutout/music calls to their own capabilities. Use for the vignette format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Discover newsletters in a target niche relevant to your ICP, evaluate audience fit, estimate reach and CPM, and output a ranked shortlist of sponsorship opportunities. Uses web search to find newsletters, then scores each against ICP alignment criteria. Use when a marketing team wants to reach an existing engaged audience for less than the cost of building their own, or when testing a new channel before committing.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Research a specific competitor across their website, reviews, ads, social presence, and pricing — then produce a structured sales battlecard with positioning traps, objection handlers, landmine questions, and win/loss themes. Chains web research, review mining, and ad intelligence. Use when sales needs competitive ammo or when entering a new market with established incumbents.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Create faithful studio, lifestyle, and on-model product photography through the GooseWorks Product Photos workflow, then approve selected results for reuse in future creative work.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Render a 'brand identity reveal' video from a config — a single poster frame in a real, softly-lit space (real wall, soft-focus plant in the corner, dappled leaf shadow, illuminated poster) whose artwork HARD-CUTS through ~10 on-brand poster mockups (hero product, IG post, hanging banners, sticker sheet, logo lockup, poster, two lifestyle stills, packaging, big icon) then holds on a brand end card. The environment plate is one create-image-fal generation; the mockups are real-DOM HTML frame-stepped via Playwright, perspective-composited into the detected frame quad with the plate's real leaf-shadow multiplied back onto each poster (reads as behind glass), sequenced by FFmpeg. Deterministic assembly, FREE (the plate comes from create-image-fal, the bed from create-music-elevenlabs), music bed only and approved brand copy only. Use for the brand-identity-reveal format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a 3D-CGI app sizzle — nano-banana CGI plates (blank-glow floating phone + a CGI studio with rim-light + placeholder burst shapes; studio look and phone finish come from the recipe choices) plus PIL compositing of the REAL App Store screenshots onto the bezel + burst-out overlays, driven by Kling 3.0 i2v steady-float per beat with a per-beat Ken-Burns FFmpeg push-in fallback when Kling garbles the UI, then VO/music mix (sidechain duck, loudnorm) + 1.15x speed + anti-AI grain finalize. The on-screen UI, any faces in it, and the wordmark are ALWAYS real assets composited via PIL — never AI-rendered. The paid steps (plates, i2v clips, VO, music) are separate capabilities; this ships the config + PIPELINE + FREE assembly and the recipe orchestrates the spend. Use for the cgi-app-sizzle video format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local Whisper (not the raw VO — concat drops inter-scene silence) and word-level captions burned in the chosen style. This is the FREE deterministic assembly + caption stage (two-zone composite + blurred fill + divider + hard-concat + end card + captions); the VO comes from create-vo-elevenlabs, the anchor from create-image-gpt-image-fal, and the whole-VO lip-sync from a paid VEED Fabric 1.0 take (a no-atom upstream input). Use for the split-screen-creator format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a creator picture-in-picture product-listicle ad from a config — the creator stays FULL-FRAME the whole beat (voice plus lips generated together per beat, no separate VO, no cut to a full-frame product shot), and on each product beat three persistent overlays ride on top for the WHOLE beat — a title pill top-center, the DEMO in a rounded PiP window top-right (the brand's real UGC clip MUTED, or for a no-UGC brand the product's own autocropped UI still / screen-recording sized to fill the window), and a bottom product card (rounded thumbnail plus 'N · CATEGORY' small-caps plus product NAME in a serif face). Hook plus CTA beats are the creator full-frame with the title pill only. Assembly builds ONE full-1080x1920 transparent overlay PNG per beat, overlays it on the creator clip (cover-scaled to 1080x1920) for the whole beat keeping the native audio, concats all beats, then burns captions LAST as timed PIL PNG overlays (this ffmpeg has no libass) timed deterministically from the known per-beat script. This is the FREE deterministic assembly stage (overlay-PNG build plus cover-scale composite plus concat plus PIL-PNG caption burn); the creator anchor and the N native talking clips come from create-image-fal (Seedream v5 Pro) and create-video-fal (Seedance 2.0). Use for the creator-pip-listicle format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Assemble a multi-scene GRWM beauty-demo ad from a config — a locked-identity creator applies ~5 products step by step while a SEPARATE ElevenLabs voiceover narrates and every scene cut is snapped to the VO's product-name word-starts (Whisper word-level timestamps), then ~5 Playwright product overlay cards (real PDP-verified taglines) are composited onto the master each on its product-NAME word-start, the SEPARATE VO is mixed on top of a ducked music bed at loudnorm I=-14, clean-white 3-words/cue captions are burned, and the video closes on a flat-lay end card. This is the FREE deterministic assembly stage (re-cut to the VO word-starts, hard-concat, Playwright card render + card composite, VO plus music mix, caption burn, flat-lay end card); the VO, scene clips, product cutouts, and music come from create-music-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the glassy-matte-grwm format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Render a 'mosaic-grid-reveal' video from a config — a real-DOM FULL-BLEED N×N mosaic of real product tiles that pops in one tile at a time (scatter order, ease-out-back overshoot), the grid clears, then the brand wordmark builds line-by-line followed by a sub-label, tagline, and CTA; frame-stepped via Playwright and encoded with FFmpeg — deterministic assembly, FREE (the music bed comes from create-music-elevenlabs), so the wordmark, tile captions, and CTA stay pixel-crisp. Use for the mosaic-grid-reveal format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Render a configurable iMessage conversation inside a properly framed phone, then a brand end card. Uses the original send/receive sounds and a shared frame timeline for text, typing, scrolling and sound. Free local Playwright + ffmpeg assembly; optional image/music generation belongs to separate gated capabilities.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
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.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Build the deterministic PIL pill overlays for a 'perfect-score + proof-points' UGC video ad (white 10/10 score header with a medal + orange sub with a finger-down + 3-4 green-check proof pills) and composite them onto a base clip in a diagonal L->R->L->R cascade, then mux the music bed into master-final.mp4. Config-driven (config.json), 1080x1920 9:16, FREE and deterministic (no paid calls, text stays pixel-crisp). Use for the overlay-proof-points format; the base clip + music come from separate paid capabilities.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Render a 'search-grid' (Pinterest search-moodboard) video from a config — a real-DOM page with four continuous beats (masonry search grid + typing hook with counter-drift columns → 3 cards slide in from the right and stack → the top card box-grows to fullscreen then swipe-left ×2 through captioned feature shots → end card), frame-stepped via Chromium and encoded with FFmpeg — deterministic assembly, FREE (real brand photos + logo; the optional music bed comes from create-music-elevenlabs), so type/logo/photos stay pixel-crisp. Use for the search-grid format.
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。
gooseworks-ai/goose-skills☆ 1,2542026年10月17日 更新
Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
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。
microsoft/skills-for-fabric☆ 1,2522026年10月5日 更新
Compare two or more companies, products, or platforms across pricing, features, positioning, and docs. Use this skill whenever the user says "compare X vs Y", "how does X stack up against Y", "alternatives to X", "competitive landscape of …", "X vs Y vs Z", or asks for a competitor matrix. Uses search to discover competitors when the user only names a category, then scrape for each competitor's homepage, pricing page, and features/docs. Returns a normalized comparison matrix as JSON.
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。
firecrawl/web-agent☆ 1,2472026年4月20日 更新
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.
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。
NyxTides/ppt-image-first☆ 1,2282026年5月8日 更新