本文へ移動
cccskills
無料GitHub で公開

render-brand-identity-reveal

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.

インストール方法を見る

含まれるファイル(11)

  • SKILL.md5.4 KB
  • scripts/build_video.sh1.2 KB
  • scripts/composite.py3.0 KB
  • scripts/config.example.json3.9 KB
  • scripts/measure_frame.py1.7 KB
  • scripts/PIPELINE.md3.5 KB
  • scripts/recrop.py2.2 KB
  • scripts/render_art.py1.3 KB
  • scripts/scene.html20.4 KB
  • skill.meta.json355 B
  • tests/smoke-test.md1.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

render-brand-identity-reveal

Render the 'brand identity reveal' format from a config. The signature is a single fixed poster frame in a REAL, softly-lit space (the illuminated poster-frame look of a shop, café, cinema or metro — the setting and wall are chosen per video): a real wall, a real soft-focus plant in the lower-left corner, dappled leaf shadow. The artwork INSIDE the frame HARD-CUTS (no crossfade) through 11 beats — 10 on-brand poster mockups + a brand END CARD held ~3s. Music bed only, NO voiceover; copy is baked into each mockup, never overlaid as captions.

This capability is the FREE assembly only. The paid parts are separate generic capabilities the recipe names — create-image-fal (the one-shot environment plate) and create-music-elevenlabs (the bed). Never re-implement them here.

Choices

The calling recipe asks the user these before any paid step; this capability only reads them from config. The worked example (Touchland) values are examples, never defaults.

  • setting — where the frame hangs → config.plate.setting (demo: premium boutique interior).
  • wall_style — the wall it is mounted on → config.plate.wall_style (demo: sage subway tile).
  • music — the bed's genre/mood → config.music.direction (demo: upbeat playful pop).

Three layers

  1. Environment plate (PAID, create-image-fal, flux-pro ultra, 9:16) — an empty lit poster frame in a real space, high-res so the camera can be pushed closer by cropping. Pick a wall color that makes the brand's poster colors POP (complement of the dominant hue).
  2. Mockups (FREE) — real-DOM HTML/CSS (scene.html), one poster per beat, built from the brand's real assets + approved copy, frame-stepped via Playwright (render_art.py, bare mode, device_scale_factor 2).
  3. Composite (FREE) — measure_frame.py detects the blank poster interior quad; composite.py perspective-warps each poster into it AND multiplies the plate's real leaf-shadow/light back onto the poster (so it reads as behind glass) + a glass sheen; build_video.sh sequences the frames with the music bed.

Inputs

  • config.json — copy scripts/config.example.json (the Touchland worked example) and edit (canvas, plate prompt + setting + wall_style from the user's choices, camera crop, per-beat durations, end-card copy). Schema + per-file working-dir layout are documented in scripts/PIPELINE.md.
  • The brand's REAL product/packaging/lifestyle stills, wordmark, and icon (recreate the icon as an SVG <mask> if the only source has an occluding element). Approved copy only.

Workflow (free assembly)

CAP=skills/ads/capabilities/render-brand-identity-reveal
RUN=<project>/working    # holds scene.html + assets/ + the create-image-fal plate in bg/

python3 $CAP/scripts/recrop.py 0.72 0.13      # camera distance (bigger frac = bigger frame); src = first bg/plate_*.png
python3 $CAP/scripts/measure_frame.py         # detect the blank poster interior quad
python3 $CAP/scripts/render_art.py            # render each poster standalone (Playwright, dsf 2)
python3 $CAP/scripts/composite.py             # warp into frame + shadow multiply + sheen
bash    $CAP/scripts/build_video.sh $RUN/concat.txt $RUN/music.mp3 out.mp4 13.05 1.0

Then watch the master (music-only, every poster legible, shadow falls across the art, end card holds). See scripts/PIPELINE.md for adapting scene.html per brand.

Rules

  • Approved brand copy ONLY — never invent claims, customers, results, or testimonials.
  • Toggle beats with OPACITY, not display (a per-state display:flex silently overrides a display:none toggle and paints one state over all others).
  • Camera distance = re-crop the high-res plate (recrop.py), don't regenerate.
  • The realism trick = MULTIPLY the plate's real shadow/light back onto each composited poster.
  • Music bed only, no VO. Close on the brand end card.

Failure Modes

  • Every rendered frame identical → a per-beat display: rule beat the visibility toggle; use opacity.
  • Playwright evaluate(fn, arg) didn't switch state in a loop → bake the beat index into the JS string and return the applied state to assert it.
  • Composited poster looks pasted-on → you skipped the shadow multiply (shadow_strength ~0.85).
  • Studio product won't cut out (white cap == seamless bg) → present as a photo-tile, don't corner-flood-fill.
  • Playwright missing under node → use the python playwright + cached chromium.

レビュー

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。

同じリポジトリのスキル

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。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

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。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

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。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

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。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

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。

gooseworks-ai/goose-skills1,2452026年10月13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-skills1,2452026年10月13日 更新

gooseworks-ai のスキルをすべて見る

このスキルの問題を報告する