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.
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Build the deterministic PIL/FFmpeg overlays for the "Instagram comparison-tool reviewer" UGC ad — a white "we got a perfect 10/10 score" headline pill (trailing medal), an orange "but here's also why you'll love us" sub pill (trailing finger-down, width-matched to the header), and 3-4 green-check proof pills — then composite them onto a base clip in the format's signature diagonal cascade and mux the music into the master. FREE and deterministic: the pills are PIL-rendered so the score, checks, and wordmark stay pixel-crisp (a video model would smear type). The base clip (create-image-fal keyframe -> create-video-fal i2v) and the music bed (create-music-elevenlabs) come from separate paid capabilities; this one only does the free rendering.
fetch_icons.py --run-dir <run> ; build_overlays.py --config config.json --out-dir <run>/generated/overlays ; compose_master.py --config config.json --run-dir <run> — reads <run>/generated/clip-handheld.mp4 + generated/music-bed.m4a, writes <run>/master-final.mp4. 1080x1920, deterministic, $0. (Add --no-music to compose for a silent design preview.)
fetch_icons.py — downloads the three Twemoji PNGs (medal 1f3c5, finger-down 1f447, check 2705) to <run>/assets/icons. PIL cannot render Apple Color Emoji, so pills paste Twemoji PNGs. Free, local.build_overlays.py — PIL: renders the white score header (trailing medal), the orange subhead (trailing finger-down, width-matched to the header), and N green-check proof pills auto-sized to their copy. Bold weight and icon-centered-on-pill-middle are load-bearing.compose_master.py — FFmpeg: scale/crop the base clip to 1080x1920@30, composite the always-on headers, cascade the proof pills (each enable='gte(t,T)' on its own alternating LEFT/RIGHT row), mux the music, apply the anti-AI grain pass, re-encode crf23/maxrate12M -> master-final.mp4.config.json (overlays, layout, duration_sec, optional music/post_production); the template recipe supplies the config from recipe.config.build_overlays.py before compose_master.py — the compositor reads pre-rendered PNGs and silently reuses stale ones on a copy change.Pillow + ffmpeg. No API keys.overlays.header.lines, overlays.subhead.lines, overlays.proof_points) is per brand and must be the brand's own approved claims; build_overlays.py exits with a clear message if any of it is empty (the template recipe ships it empty, with overlays._example_content as the shape).This atom renders no people, setting or music. The template's creative choices (whose hand, the setting, the music style) are asked of the user by the recipe and go to the paid keyframe / i2v / music capabilities; this atom only composites the pills over whatever base clip and music bed it is given.
Read the music preparation instructions for the composition options and listening checks.
The bed must cover the complete master, including its end card. Composition checks audio coverage before rendering and stops for missing, short or prematurely silent music. A silent preview requires the explicit no-music option.
Use a full-length approved bed first. For an approved instrumental that can repeat cleanly, the loop-music option trims silent edges and extends it locally with crossfades. It preserves the source and puts the final half-second fade at the master ending. Do not loop speech, lyrics or a musical ending that makes the join obvious. Do not regenerate paid music automatically.
Listen to every join and the final seconds before delivery. The automated check detects silence and insufficient duration; it does not judge musical phrasing, a gradual early fade or how the bed sounds under dialogue.
The renderer preserves the bold text font and falls back to an installed symbol font for missing glyphs such as ★. Trailing icons sit beyond the longest line of the pill. The composer reserves at least 120 px at the right edge for platform controls and stops if copy does not fit; wrap the text or reduce its font size before rendering.
Run the free regressions with python -m unittest discover -s tests -p 'test_*.py' -v.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
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。
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.
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。
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.
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。
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.
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。
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.
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。
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。