Video Production

Video Production Skill

Generate cinematic video clips with Veo 3, review them in a browser preview, iterate with feedback, and assemble final A/B test videos — all with minimal token spend.

Quick Start

cd ~/.openclaw/workspace/skills/video-production # 1. Generate all clips from storyboard .venv/bin/python3 scripts/batch_generate.py --storyboard /path/to/storyboard.json # 2. Open browser preview .venv/bin/python3 scripts/generate_preview.py --storyboard /path/to/storyboard.json # 3. (After feedback) Re-generate only revised scenes .venv/bin/python3 scripts/apply_feedback.py --storyboard storyboard.json --feedback feedback.json # 4. Assemble final video .venv/bin/python3 scripts/ffmpeg_assembler.py --storyboard storyboard.json

A/B Video Architecture

Target: 15-second videos, 3 clips × 5s each [HOOK: 5s] → [CORE: 5s] → [CTA/PAYOFF: 5s] ↑ ↑ swap for A/B swap for A/B Economics: 5 Veo prompts → 4 unique A/B videos (2 hooks × 1 core × 2 CTAs) 7 prompts → 9 videos | 9 prompts → 16+ videos Transitions at 5s and 10s marks — clean for analytics

Pipeline Overview

storyboard.json ↓ batch_generate.py → clips/scene_01.mp4 ... scene_05.mp4 ↓ generate_preview.py → preview.html (opens in browser, zero tokens) ↓ [review + paste feedback JSON to Muffin] ↓ [Muffin suggests revised prompts, updates storyboard.json] ↓ apply_feedback.py → re-generates only 'revise' scenes ↓ ffmpeg_assembler.py → final_AA.mp4, final_BA.mp4, final_AB.mp4, final_BB.mp4 Token cost: Only when writing storyboard + interpreting feedback. Preview, generation, and assembly are all zero tokens.

Storyboard Format

{ "project": "my-video", "output_dir": "clips", "final_output": "final.mp4", "scenes": [ { "id": "scene_01", "role": "hook_a", "label": "Hook A", "order": 1, "duration": 5, "aspect_ratio": "16:9", "prompt": "..." } ], "_ab_combinations": { "video_1_AA": ["scene_01", "scene_03", "scene_04"], "video_2_BA": ["scene_02", "scene_03", "scene_04"], "video_3_AB": ["scene_01", "scene_03", "scene_05"], "video_4_BB": ["scene_02", "scene_03", "scene_05"] } } See scripts/storyboard_template.json for full template.

Feedback Format

Paste this JSON to Muffin after reviewing preview.html: { "scenes": [ { "id": "scene_01", "action": "approve", "notes": "" }, { "id": "scene_02", "action": "revise", "notes": "slower camera, warmer light" } ] }

Veo 3 API — Current Limits (Gemini API, verified 2026-02-23)

ParameterSupportedaspect_ratio✅number_of_videos✅negative_prompt✅duration_seconds❌ Broken (throws 400 even with valid values)fps❌ Vertex AI onlycompression_quality❌ Vertex AI onlyenhance_prompt❌ Vertex AI only Models: veo-3.1-generate-preview (best) | veo-3.1-fast-generate-preview | veo-3.0-generate-001 SDK: google-genai (NOT google-generativeai)

Prompting Techniques

Motion in every sentence — Veo produces laggy output from static prompts. Every sentence should describe camera OR subject movement. Character continuity — Veo can't maintain exact characters across clips. Describe physical details explicitly in every scene that includes the same character. ✅ "The same client character from the opening — dark jacket, professional bearing, 30s-40s" Stitch continuity — For seamless cuts, open each prompt with the color/light state the previous clip ends on. ✅ "Warm amber light, a direct visual continuation from the post-production suite..." Single continuous shot — Each prompt is one continuous clip. Design it as one camera move that reveals multiple elements — not a montage description. Content policy — Environmental/prop-only scenes generate reliably. Stressed people on phones can silently return no video. Keep humans calm or describe the environment instead.

Quota Management

When you hit the daily limit (429 RESOURCE_EXHAUSTED), use the quota watcher: # Sets a cron that retries every 30 min, texts Master when done chmod +x scripts/quota_watcher.sh # Add to crontab: (crontab -l 2>/dev/null | grep -v quota_watcher; \ echo "*/30 * * * * /path/to/quota_watcher.sh >> /tmp/quota_watcher.log 2>&1") | crontab - See api-quota-watcher skill for the generic pattern.

Scripts

ScriptPurposescripts/batch_generate.pyGenerate all scenes from storyboard, skip existingscripts/generate_preview.pyBuild preview.html with video players + feedback formscripts/apply_feedback.pyRe-generate only scenes marked 'revise'scripts/ffmpeg_assembler.pyStitch approved clips → final MP4 (cut or crossfade)scripts/quota_watcher.shRetry + notify cron for quota recoveryscripts/storyboard_template.jsonStarting storyboard template

Environment Setup

cd ~/.openclaw/workspace/skills/video-production uv venv .venv uv pip install google-genai Pillow requests # API key must be in ~/.zshenv: export GOOGLE_API_KEY="AIza..."

Assembling A/B Combinations

After all scenes approved, run assembler for each combo: # Assemble all 4 A/B videos for combo in AA BA AB BB; do # Edit storyboard or pass scene list directly .venv/bin/python3 scripts/ffmpeg_assembler.py \ --storyboard storyboard.json \ --output "final_${combo}.mp4" done Or hardcode in _ab_combinations in storyboard.json — assembler reads it automatically.

Format Adaptation

FormatNotes16:9 (master)Default — all scripts use this9:16 (vertical)Change aspect_ratio to "9:16" in storyboard1:1 (square)Change aspect_ratio to "1:1" Generate separate storyboards per format for best results. Don't crop 16:9 to 9:16 in post — re-generate with proper aspect.

What Veo 3 Does Well

Atmospheric/mood shots Smooth camera movements (push-in, crane, tracking) Lighting transitions within a single clip Office/studio/urban environments Abstract beauty (nature, space, product)

What Veo 3 Struggles With

Exact text on screen (add in post via After Effects/Resolve) Maintaining character consistency across clips Very fast montage within a single generation Complex multi-person scenes Specific prop/brand details

Clean Slate Default

Every new campaign starts fresh. No inherited characters, no assumed cast, no prompt weights from previous runs. If you want continuity from a past campaign, explicitly say so: "Use HERO_01 from the MMM campaign"

Character IDs (Bootstrap Defaults)

If no cast is defined, use these placeholders: HERO_01 — Primary UGC creator FRIEND_01 — Recurring side character HAND_MODEL_01 — Hands-only product handler First approved output becomes the canonical identity baseline for that campaign.

Character Bible (Per Campaign)

When characters are defined, maintain a character_registry.json in the project folder: { "HERO_01": { "identity": { "age_range": "28-35", "gender": "male", "skin_tone": "...", "hair": "...", "build": "..." }, "wardrobe": { "preferred": [], "avoid": [], "signature": "" }, "camera_rules": { "preferred_framing": "medium close-up", "avoid": [] }, "negative_constraints": [], "reference_frames": [], "phrase_weights": {} } }

CAST Block Injection

• When characters are defined, every prompt must include:
• CAST:
• HERO: HERO_01 (identity locked; must match reference frames exactly)
• Do not alter identity traits across frames or across future assets.

Verification Thresholds

After generation, run vision model consistency check against reference frames: >= 85 → auto-pass 75–84 → escalate to Master (Telegram), do not auto-regen <= 74 → auto-fail, apply stabilize patch, retry once → then escalate if still failing

Learning Loop

After every human review decision, update: Approved → increase weights for phrases that produced good consistency; add best frames to approved reference set Rejected → identify drift attributes; downweight or ban phrases causing drift; add negative constraints Borderline → apply stabilize patch for that engine+character+scene combo

Generation Log

Append every attempt to generation_log.jsonl (never deleted): { "timestamp": "...", "campaign": "...", "scene_id": "...", "engine": "veo-3.1-generate-preview", "attempt": 1, "characters": ["HERO_01"], "prompt": "...", "output": "clips/scene_01.mp4", "verification_score": 88, "drift_notes": "", "decision": "auto_pass", "human_outcome": "approved", "worked_phrases": [], "failed_phrases": [] }

Escalation Policy — Ask Before Guessing

Escalate to Master via Telegram (never silently loop) when: Verification score is borderline (75–84) Character is on a new engine for the first time Scene type is new for that character+engine combo Same prompt has failed 2+ times in a row Escalation message must include: scene ID, engine, score, drift notes, and 2–3 options.

Archive (Persists Across Campaigns)

Even though each campaign starts clean, these persist in the skill folder: generation_log.jsonl — full audit trail approved_references/ — canonical frames by campaign, available to load on request campaign_phrase_weights/ — weight archives per campaign, loadable for continuity