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.
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Render the 'search-grid' format from a config. It is a deterministic assembler — no
generative image/video, no AI-rendered text. Everything on screen is the brand's REAL
product/lifestyle photography + logo, so the typed hook, feature captions, wordmark, and
photos stay pixel-crisp. The only paid input is an optional music bed, produced upstream
by create-music-elevenlabs and passed to render.py --music.
stack_bg + stack_tint).transform:scale, which would
stretch the image), then swipe-left → swipe-left through the SAME 3 rooms, each now
full-bleed with a caption. The 3 cards ARE the 3 features.endcard.bg.scripts/build_html.py --config config.json --out index.html — config → a single
self-contained HTML page exposing window.seek(tMs) (images base64-embedded).scripts/capture.js --html index.html --out frames --fps 30 --duration 18000 — headless
Chromium frame-steps seek() to a PNG per frame (auto-discovers a cached Playwright
chromium, or pass --exe).scripts/render.py --config config.json [--music bed.m4a] --out master.mp4 — orchestrates
build → capture → FFmpeg (muxes the bed with -map 0:v:0 -map 1:a:0 when --music is
given; $0 silent pass without it).scripts/prep_assets.py crop in.jpg out.jpg / logo logo.jpg wordmark.png — the two
fixes this format needs almost every time: crop baked-in white L/R margins off heroes, and
key the white out of a black-on-white logo JPG to a transparent PNG.See scripts/config.example.json for the full config shape (a real worked example — the
Ruggable build; its warm beige backdrop, red caret and lo-fi music prompt are that demo's
picks, never defaults).
These look values are the caller's creative choices (the video-format recipe asks the user):
endcard.bg, stack_bg, stack_tint. The demo used a warm beige
radial gradient (#ecdfce → #d9c6ac) and a warm brown tint.create-music-elevenlabs).
The demo used warm nylon acoustic guitar + Rhodes, relaxed lo-fi.caret_color is the brand's accent colour (brand kit), not a choice.When caret_color, endcard.bg or stack_tint is missing or still an unfilled
<input:…> / <brand:…> placeholder, build_html.py uses a NEUTRAL value and prints a
WARNING; an unfilled text/path field (hook, rooms, endcard.hero, …) is an error.
canvas, hook, grid_cols (3 columns × 6 distinct tiles), rooms (exactly 3
{image, caption} — the cards AND the features), stack_bg (blurred backdrop photo), optional stack_tint (CSS background over it),
endcard (hero, wordmark, tagline, cta, bg). Craft rules the renderer assumes the
inputs already honor: real brand assets only; 6 distinct tiles/column so the drift never
repeats; intentional (never near-white) backdrops in the mood the caller picked; cropped hero margins; transparent-bg wordmark;
captions/tagline/CTA are the brand's OWN approved copy (no invented claims).
node + playwright-core (or a cached Playwright chromium) and ffmpeg on PATH; python3
with Pillow (for prep_assets.py).
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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