viral-video-replicator

name viral-video-replicator description 视频逆向复刻 — 分析参考视频(FFmpeg帧提取+Vision LLM) + 生成复刻Seedance 2.0 Prompt + 4种素材替换模式。支持单个和批量。Use when: '复刻这个视频', '分析爆款视频', 'replicate this video', '视频逆向', '反编译视频', '批量分析视频'. Do NOT use for creating from scratch — use fashion-video-creator instead. version 1.1.0 compatibility Claude Code, Claude.ai (with local FFmpeg), and all SKILL.md-compatible agents dependencies ["ffmpeg","ffprobe"] Skill: viral-video-replicator Overview Reverse-engineer reference videos (e.g., competitor viral content) into replicable Seedance 2.0 prompts. The pipeline: FFmpeg frame extraction -> contact sheet grids -> audio extraction + ASR transcription -> Vision LLM structured analysis -> Seedance prompt assembly with optional material replacement (face/body/clothing). Supports single and batch modes. When to Activate User query contains any of: "视频复刻", "视频逆向", "反编译视频", "复刻爆款" "分析这个视频", "replicate this video", "video analysis" "批量分析", "批量复刻" "这个视频怎么拍的", "帮我分析一下这个爆款", "我想拍一个类似的视频" "reverse engineer this video", "analyze this fashion video" Do NOT activate for: Creating fashion videos from scratch (no reference video) -> use fashion-video-creator "帮我做个穿搭视频", "生成模特图" -> use fashion-video-creator Pure video editing / trimming -> not applicable Non-fashion video analysis -> not applicable Prerequisites Local tools (REQUIRED): # macOS brew install ffmpeg # Linux apt install ffmpeg # Verify ffmpeg -version && ffprobe -version Cloud APIs (collected via clarification): REQUIRED: ARK_API_KEY + ARK_VISION_MODEL (Vision LLM for frame analysis) CONDITIONAL: ASR_ACCESS_TOKEN (if video has dialogue) CONDITIONAL: TOS credentials (if ASR is needed — audio transfers through TOS) Clarification Flow Phase 1: API Key Acquisition Ask IN ORDER. Use plain language — explain WHY each service is needed. Q1: Vision Analysis (REQUIRED) "分析视频内容需要一个能'看懂图片'的 AI 模型。 它会看视频的截图，识别出人物长相、服装细节、场景布局、动作时间轴。 你有火山方舟的账号和 API Key 吗？还需要视觉模型的ID。" If no API key -> STOP. Guide user to 火山方舟. Do NOT proceed. Q2: Speech Transcription (CONDITIONAL) "参考视频里的人有说话吗？ 如果有对话，需要用语音转文字来提取台词 — 这样复刻出的视频才能有完整的对白内容。 没有对话的纯画面视频可以跳过这步。" If yes -> ask for ASR_ACCESS_TOKEN. Q3: Audio Storage (CONDITIONAL — only if Q2 = yes) "语音转文字需要通过云存储传输音频文件。 需要火山引擎对象存储(TOS)的 4 个信息：Access Key, Secret Key, Bucket, Region。" If user has ASR but no TOS -> warn: "没有 TOS 则 ASR 无法工作，等同于没有语音转录。" Phase 2: Mandatory Recommendations MUST show. Each item has WHY explanation: ============================================================ API Configuration — Mandatory Recommendations ============================================================ [REQUIRED] Vision model: doubao-seed-1-6-vision-250815 or newer WHY: Older models cannot distinguish clothing fabric textures (acetate vs chiffon), stitching details (overlocked vs raw edge), or fit nuances (slim vs A-line). Analysis quality drops ~60%. [REQUIRED] If video has dialogue: configure BOTH ASR + TOS WHY: Without ASR, all spoken content is lost. The generated prompt will only contain visual descriptions. Video fidelity drops from ~90% to ~50% because dialogue drives 40%+ of viewer engagement. TOS is the audio transfer pipeline — no TOS means no ASR. [REQUIRED] Video resolution: 720p or higher WHY: Frames are extracted at 360x640 thumbnails. Source below 480p means thumbnails are upscaled garbage — clothing patterns and textures become unrecognizable blobs. [RECOMMENDED] Exact mode for same-category replacement WHY: "exact" does nested structured analysis (10 fields with typed subobjects) — precision matters when replacing one dress with another. "rewrite" does flat analysis (10 string fields) — better for extracting viral logic across different product categories. ============================================================ Phase 3: Mode Selection "你要分析几个视频？单个还是批量？" Q5: Replicate Mode (per video if batch) "你想怎么复刻？ 精确复刻 : 逐帧分析每个细节，尽可能1:1还原 提取改写 : 提取爆款节奏和逻辑，用新方式重新演绎" Q6: Material Replacement (per video if batch) "要替换视频中的哪些元素？ 不换（纯复刻） 换人脸/身材（上传模特参考图） 换衣服（上传商品图） 都换（上传模特图 + 商品图）" Batch-Specific Recommendations ============================================================ Batch Mode — Additional Recommendations ============================================================ [REQUIRED] ALL videos should be 720p+ WHY: One low-res video doesn't just fail for itself — it wastes API costs on a Vision LLM call that returns unusable analysis. [RECOMMENDED] Pre-sort by replicate mode WHY: exact mode takes 2-3 min/video (nested analysis), rewrite takes 1-2 min/video (flat analysis). Grouping avoids context switches. [WARNING] Each video runs the FULL pipeline independently. N videos = approximately N * 2-3 minutes. Plan accordingly. ============================================================ Four Replacement Modes Mode What User Uploads @image Tags in Prompt What Gets Replaced clone Nothing None (pure text) Nothing — exact replication face_swap Face/body reference @image1 = face ref Person replaced, clothing preserved outfit_swap Garment product image @image1 = garment Clothing replaced, person preserved full_swap Garment + face reference @image1 = garment, @image2 = face ref Both replaced Mode auto-determination: has_person_ref AND has_garment_ref -> full_swap has_garment_ref only -> outfit_swap has_person_ref only -> face_swap neither -> clone Core Workflow Step 0: Environment Check (mandatory, never skip) ffmpeg -version && ffprobe -version Returns version -> proceed to Step 1 command not found -> guide install (brew/apt/choco). Still fails after install -> Soft fallback: Ask user: "FFmpeg 不可用，你能手动提供视频截图和音频文件吗？" If user provides frames manually -> skip FFmpeg steps, proceed from Step 4 (Vision analysis) with user-provided images. Quality warning (MUST show to user): "手动截图模式下分析质量会显著降低：无精确时间戳标注、无均匀3fps采样、帧数可能不足导致动作时间轴不准确。建议安装 FFmpeg 以获得最佳效果。" If user cannot provide frames -> STOP. FFmpeg is required for automated extraction. Step 0b: Verify API Key (before reaching Step 4) Validate ARK_API_KEY early to avoid wasting FFmpeg processing time on an invalid key: If bash/Python available: resp = httpx.get( f" {ARK_API_BASE} /api/v3/models" , headers={ "Authorization" : f"Bearer {ARK_API_KEY} " }, timeout= 10 ) 200 -> proceed 401/403 -> STOP. Key invalid. Fix before continuing. If no code execution: Trust user-provided key, validate on first Vision API call. Single Mode Step 1: Collect API keys + mode + replacement materials Step 2: Extract frame grids (3fps) + extract audio — PARALLEL via asyncio.gather() (Both are FFmpeg subprocesses launched concurrently in Python, not LLM-level parallelism) Read references/frame-extraction.md for FFmpeg specs Step 3: Upload audio to TOS -> ASR transcription Read references/asr-pipeline.md for protocol Step 4: Vision LLM analysis (grids + transcript -> structured JSON) Read references/vision-analysis.md for exact vs rewrite schemas Step 5: Determine replacement mode from uploaded materials Step 6: Assemble Seedance 2.0 prompt Read references/reverse-prompt.md for 4-mode assembly Step 7: Generate mode-specific SOP Step 8: Validate output (see below) Step 9: Return: prompt + analysis + transcript + SOP + replacement summary Batch Mode Step 0: Verify FFmpeg Step 1: Collect API keys + video count + per-video configs Step 2: For each video (sequential): a. Extract frame grids + audio (parallel) b. TOS upload -> ASR transcription c. Vision LLM analysis d. Determine replacement mode e. Assemble prompt f. Generate SOP g. Validate this video's output h. Mark: completed / failed Step 3: Return all results with progress summary Progress: queued -> processing -> completed/failed Partial success: batch completes even if some videos fail. Output Validation (mandatory, never skip) Before delivering results, verify ALL: Analysis JSON is valid and contains all required fields? Prompt correctly uses @image tags matching the replacement mode? If clone mode: prompt has NO @image references (pure text)? If outfit_swap/full_swap: prompt includes "Do not alter clothing pattern, color, texture or style"? If has_speech: dialogue content is present in prompt (not empty)? SOP upload instructions match the number of images for this mode? Replacement summary correctly lists what was preserved vs replaced? Any NO -> fix before delivering. Do NOT send unvalidated output. Error Handling Failure Detection Action FFmpeg not installed command not found STOP. Provide install command. Do NOT proceed. No API key ARK_API_KEY empty STOP. Guide user to 火山方舟. Do NOT proceed. Vision model error 4xx/5xx from API Report error with model ID used. Suggest checking model availability. Vision returns invalid JSON JSON parse fails Retry once with same grids. Still fails -> report raw response for debugging. Frame extraction fails FFmpeg non-zero exit Check video format. Try re-encoding. Report if still fails. No audio track extract_audio returns None Skip ASR. Proceed with visual-only analysis. Note in output: "No audio detected." TOS upload fails Upload exception after 2 retries Skip ASR. Proceed visual-only. Warn: "Audio transcription unavailable — dialogue will be missing." ASR timeout No result after 120s Skip transcript. Proceed visual-only. Warn: "Speech transcription timed out." ASR silent audio Status 20000003 Normal — video has no speech. Proceed with visual-only. Video too large >200MB Reject immediately. Ask user to compress or trim. Batch video fails Exception during pipeline Mark failed with error. Continue remaining. Report partial results. Degraded Modes (graceful degradation chain) Failure Point Degraded Mode What User Still Gets Quality Impact ASR fails (TOS/timeout) Visual-only analysis Prompt with visual descriptions, no dialogue ~50% fidelity — all spoken content lost Vision exact mode fails Auto-retry with rewrite mode Flat analysis (less precise) ~70% fidelity — loses nested structure (clothing/scene subfields) Vision rewrite also fails Return raw materials Frame grids + transcript for manual analysis ~20% — no automated analysis, user must write prompt manually Seedance prompt assembly fails Return analysis only Analysis JSON + transcript ~30% — user has data but no ready-to-use prompt FFmpeg unavailable (user provides screenshots) Manual frame mode Analysis from user-provided images ~40% — no timestamps, uneven sampling, incomplete frame coverage Always prefer delivering partial results over delivering nothing. Every degraded output MUST clearly state: (1) what is missing, (2) why, and (3) the estimated quality impact. See references/fallbacks.md for detailed recovery procedures per failure case. Usage Example Input: "帮我复刻这个爆款视频，换成我的衣服" + uploaded video (15s, 720p) + uploaded garment image Resolved: mode=exact, replacement=outfit_swap (garment_ref provided, no face_ref) Output 1 — Structured Analysis: { "person" : { "gender" : "female" , "age_range" : "22-26" , "face" : "鹅蛋脸，大眼睛，双眼皮" , "skin_tone" : "白皙" , "hair" : "黑色长直发，中分，自然垂落" , "build" : "纤细高挑" , "makeup" : "淡妆，裸色唇彩" } , "clothing" : { "type" : "V领碎花连衣裙" , "color" : "奶油白底+粉色碎花" , "material_look" : "轻薄飘逸雪纺" , "neckline" : "V领" , "fit" : "A字收腰" , "length" : "及膝" , "details" : "腰部抽绳系带，裙摆荷叶边" } , "scene" : { "location" : "现代公寓客厅" , "lighting_source" : "右侧落地窗自然光" } , "actions" : "0-2s: 正面微笑打招呼；2-5s: 右手拉起裙摆展示面料；5-8s: 小幅转身展示裙摆飘动；8-12s: 右手翻开裙子内侧展示车线；12-15s: 右手捏腰部展示松紧" , "dialogue" : "姐妹们你们快看...（右手拉起裙摆）这个面料是醋酸缎面的...滑滑的凉凉的..." } Output 2 — Seedance Prompt (outfit_swap):