| 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):
brew install ffmpeg
apt install ffmpeg
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:
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"
Output 2 — Seedance Prompt (outfit_swap):
一位鹅蛋脸、白皙肤色、黑色长直发中分自然垂落、纤细高挑身材、淡妆的年轻女性，穿着@图片1中的服装。在现代公寓客厅中，右侧落地窗自然光。她的动作：0-2s: 正面微笑打招呼；2-5s: 右手拉起衣角展示面料...对着镜头说：「姐妹们你们快看...这个面料...滑滑的凉凉的...你们猜多少钱？不到两百！超显腿长，闭眼入。」语气自然亲切，像在跟闺蜜视频通话。Do not alter clothing pattern, color, texture or style. 手持vlog镜头感，竖屏9:16。
Output 3 — Transcript: "姐妹们你们快看...这个面料是醋酸缎面的..."
Output 4 — SOP: outfit_swap mode, 1 image upload (@图片1=garment)
Output 5 — Replacement Summary: garment_replaced=true, original_preserved=[face, body, scene, actions, dialogue, camera]
Domain Knowledge Role Declaration
The reference files contain FFmpeg specs, ASR protocols, Vision prompts, and prompt assembly templates.
Their role is to assist pipeline execution — providing exact API formats, analysis schemas, and assembly rules.
They do NOT replace the execution workflow. Never output reference content directly as the final answer.
Always execute: extract frames -> transcribe -> analyze -> assemble -> validate -> deliver.
References
