# Send Video Production to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "video-production", "name": "Video Production", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/omerflo/video-production", "canonicalUrl": "https://clawhub.ai/omerflo/video-production", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/video-production", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=video-production", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "ORCHESTRATOR_SPEC.md", "README.md", "SKILL.md", "assets/timing_template.json", "dalle_generator.py", "references/platform-guide-veo3.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-07T17:22:31.273Z", "expiresAt": "2026-05-14T17:22:31.273Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/video-production" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/video-production", "downloadUrl": "https://openagent3.xyz/downloads/video-production", "agentUrl": "https://openagent3.xyz/skills/video-production/agent", "manifestUrl": "https://openagent3.xyz/skills/video-production/agent.json", "briefUrl": "https://openagent3.xyz/skills/video-production/agent.md" } } ``` ## Documentation ### 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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: omerflo - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-05-07T17:22:31.273Z - Expires at: 2026-05-14T17:22:31.273Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/video-production) - [Send to Agent page](https://openagent3.xyz/skills/video-production/agent) - [JSON manifest](https://openagent3.xyz/skills/video-production/agent.json) - [Markdown brief](https://openagent3.xyz/skills/video-production/agent.md) - [Download page](https://openagent3.xyz/downloads/video-production)