Brand Video

brand-video — consistent styled faceless videos, on demand

Invoke as /brand-video. One command, many looks. Built from the validated RestoreAssist run (9 videos shipped). Picks a style from styles.md, holds everything else constant so output is consistent batch-to-batch and brand-to-brand.

Inputs (ask only if missing; infer sensibly)

• brand — RestoreAssist / Synthex / DR / NRPG / CARSI / CCW / Unite, or a client. Sets claims + CTA + voice.
• style — a key from styles.md (default flat-line). This is the "look" / dropdown value.
• topic(s) — one angle, or a list for a batch (one video each, parallel).
• voice — ELEVENLABS_VOICE_ID (per-brand; from ~/.hermes/.env). CRITICAL: confirm the current value before generating — the user changes it.
• upload? — default: render + stage only. Upload to YouTube Unlisted only when asked.

Pipeline (per video — one slug folder each, never share a dir)

Visual generation (binding): all images/video route through the grounded pipeline — see .claude/rules/real-images-only.md + the grounded-visuals skill. Direct provider calls fail CI.

1. Script → <slug>/script.md: one clean VO paragraph, ~120–140 words, ~12–16 short sentences (one sentence = one visual beat = one image), problem→solution→CTA. Ground claims in the brand wiki; never invent features. End with the brand CTA line.
2. Voiceover → run the bundled tts.py (reads ELEVENLABS_API_KEY + ELEVENLABS_VOICE_ID + DIR): cd <workdir> && set -a && . ~/.hermes/.env && set +a && DIR=$PWD/<slug> python3 <skill>/pipeline/tts.py Writes <slug>/voiceover.mp3 + <slug>/transcript.json (beats). Note the beat count.
3. Images → one per beat via the grounded pipeline — generateImage()/generateBatch() in lib/services/ai/image-generation.ts (or the generate_image MCP studio tool). Each prompt = the chosen style's POSITIVE token + a concrete scene for that sentence, + the style's NEGATIVE token; the industry auto-detects from the prompt and the trained LoRA (e.g. carpet-style-v1, trigger ccwcarpet) auto-applies where relevant. Landscape 16:9. Save zero-padded <slug>/images/01.png …. No owned references for the subject ⇒ the call returns blocked: true — grow the reference library (real photos), never fall back to a direct provider. Use slug-prefixed filenames to avoid parallel collisions (see [[parallel-agents-shared-output-collision]]).
4. Stitch → python3 <skill>/pipeline/stitch.py <slug> → <slug>/final-1080p.mp4 (1920×1080@25, image timing from beats). Verify final duration ≈ voiceover duration.

Batches & new looks

• Batch: dispatch one sub-agent per topic (parallel), each owning its slug dir. See the RestoreAssist run for the exact agent prompt shape.
• Re-voice (voice changed, same script/images): regenerate VO, rescale list.txt durations by new_total/old_total, re-stitch. (revoice pattern.)
• Add a look: append a block to styles.md (positive/negative token). That key immediately becomes a selectable style — this registry IS the dropdown's data source.

Upload (YouTube, optional)

Browser file-upload is dead (host paths rejected). Use the YouTube Data API path documented in [[youtube-api-upload-restoreassist]]: Desktop OAuth client → token at ~/.hermes/yt-restoreassist-token.json → videos().insert privacyStatus=unlisted, category 27, not-for-kids; guard on channel title; idempotent via uploaded.json. Requires YouTube Data API v3 enabled in the OAuth client's GCP project.

Guardrails

• Claims must be real (brand wiki / product truth). Flat-illustration + synthetic voice → YouTube AI-disclosure usually "No" (confirm at upload; flip to Yes if AI music added).
• Keep one style per batch for consistency. Keep SKILL.md ≤200 lines per [[feedback-tight-code]].
• Reference implementation + assets: /Users/phill-mac/restoreassist-videos/.