Clone Ad

Clone ad — Seedance 2.0

Clone an existing video ad for a different product or offer. The agent analyzes the source video frame-by-frame, transcribes dialogue, extracts the visual style and beat structure, then generates a new Seedance 2.0 video adapted for the user's product.

How this differs from analyze-video:

• analyze-video → output is a reusable markdown template saved to prompt-library/
• clone-ad → output is a generated Seedance 2.0 video delivered to the user

Prerequisites

Before starting, verify:

which ffmpeg || echo "MISSING — run: brew install ffmpeg"
python3 -c "import whisper; print('whisper OK')" 2>/dev/null || echo "MISSING — run: pip3 install openai-whisper"

Both extract-frames.sh and whisper depend on ffmpeg. If missing, install via brew install ffmpeg before proceeding.

Workflow

Step 0: Gather inputs

Collect from the user:

| Input | Required | Notes | |-------|----------|-------| | Source video | yes | The video ad to clone. File path to .mp4, .mov, .webm | | Product image | recommended | Reference photo of the user's product. Becomes referenceImages / @(img1) in the prompt. Without this, Seedance invents its own product design. | | Product/offer description | if no image | Text description of the product, its features, target audience, and key selling points. Used to rewrite dialogue and product references. | | Brand voice | optional | Check MASTER_CONTEXT.md for brand blocks. If empty, ask the user for tone/audience preferences. |

If the user only provides a video and says "clone this for my product," ask them for at least a product image or a text description before proceeding.

Step 1: Extract frames and audio

Reuse the analyze-video extraction script — do NOT duplicate it.

bash "skills/arcads-external-api/prompting/analyze-video/scripts/extract-frames.sh" \
  "" "/tmp/clone-ad-analysis" 

Frame count by duration:

| Source duration | Frames | |-----------------|--------| | Under 10s | 8 | | 10–20s | 12 | | 20–30s | 16 | | Over 30s | 20 |

Outputs:

• frame_001.jpg through frame_NNN.jpg
• audio.wav (16 kHz mono, whisper-ready)
• metadata.txt (duration, resolution, fps, frame count)

Read metadata.txt to get the source video duration — you'll need it for step 6.

Step 2: Transcribe audio

Use whisper to get the exact dialogue. This is critical — the dialogue pattern is what gets adapted for the user's product.

import whisper
model = whisper.load_model("base")
result = model.transcribe("/tmp/clone-ad-analysis/audio.wav")

Record:

• Full transcript text
• Per-segment timestamps and text (result["segments"])
• Total word count
• Language detected

If the video is silent (no speech detected), note that and skip the dialogue adaptation in step 7. The clone will be a visual-style clone only.

Step 3: Compressed analysis

Read ALL extracted frames visually. For each frame, note:

Structure and pacing:

• How many distinct beats/shots are there?
• What's the narrative arc? (hook → demo → verdict? reveal → detail → CTA?)
• How long does each beat last? (map to segment timestamps)

Camera and framing:

• POV style: selfie/handheld, tripod, propped phone, over-the-shoulder?
• Framing per beat: wide, medium, close-up, macro?
• Camera movement: static, pan, dolly, handheld shake?
• Signature framing moves (e.g., "leans into camera," "tilts product toward lens")

Edit style:

• Transition type: jump cuts, dissolves, match cuts?
• Visual rhythm: fast cuts vs held shots?
• Any recurring motif (e.g., "every other beat is an extreme close-up")?

Dialogue and script structure:

• Hook format: question, statement, exclamation, reaction?
• Speech pattern: casual/formal, filler words, trailing thoughts, mid-sentence cuts?
• How many spoken lines? How many silent beats?
• CTA style: direct ("link in bio"), soft ("you need to try this"), none?

Tone and energy:

• Emotion words that describe the speaker/mood
• Energy arc: starts calm → builds excitement? Flat? Burst then settle?
• Speaker's relationship to viewer: friend, expert, skeptic, fan?

Lighting and technical quality:

• Light source: natural/artificial, direction, quality
• Camera quality: phone/DSLR/cinema, intentional flaws?
• Audio quality: phone mic, studio, car, outdoor?

Product references:

• How is the product physically shown? (held up, worn, applied, on a surface)
• What specific claims or features are called out?
• Brand mentions, labels visible, text overlays?

What makes this ad distinctive (2–3 defining traits):

• The unique combination of elements that makes this ad recognizable
• These are the traits that MUST transfer to the clone

Store this analysis internally — it does NOT get saved as a template file.

Step 4: Present analysis summary

Show the user a structured breakdown before proceeding:

📋 Source video analysis

Duration: Xs | Beats: N | Dialogue: Y words | Style: [style name]

Beat map:
  [00:00–00:03]  HOOK — close-up, excited expression, "opening line"
  [00:03–00:07]  SHOW — tilts product to camera, "feature call-out"
  [00:07–00:10]  DEMO — (silent) applies/uses product, close-up on texture
  [00:10–00:15]  VERDICT — back to camera, "closing line + CTA"

Defining traits:
  1. [trait 1]
  2. [trait 2]
  3. [trait 3]

What transfers to your product:
  ✅ Beat structure, pacing, camera angles, edit style, tone, energy
  ✅ Dialogue pattern (adapted for your product)
  ✅ Lighting and technical quality cues

What gets swapped:
  🔄 Product references → your product
  🔄 Specific claims → your product's features
  🔄 Brand mentions → your brand (if provided)

Proceed with adaptation? (yes / adjust)

Wait for user confirmation before continuing.

Step 5: Decide generation mode

Walk through this decision tree:

┌─ Source video ≤ 15s?
│   YES → Single-clip generation
│   NO  → Multi-clip split at natural beat boundaries
│         Each clip ≤ 15s (Seedance max)
│         Identify best split points from beat map
│         Use the CHAINED MULTI-CLIP PIPELINE below
│
├─ User provided a product IMAGE?
│   YES → Image-to-video mode (referenceImages with @(img1) in prompt)
│         For multi-clip: use i2v for clip 1 ONLY, then chain v2v for clips 2+
│   NO  → Text-only mode (describe product in prompt text only)
│         OR v2v if:
│           - Source video has NO human faces
│           - AND user wants to preserve exact visual style
│           - (v2v with faces → content checker rejection + billed)
│
├─ Source video has person SPEAKING?
│   YES → audioEnabled: true (confirm with user)
│         Dialogue confirmation gate REQUIRED (step 7)
│   NO  → audioEnabled: false (or ask user preference)
│         Skip dialogue gate
│
└─ User wants voice clone from source audio?
    YES → Upload source audio as referenceAudios
          (check audio+image regression: run sanity probe first)
    NO  → Seedance generates its own voice from text

Chained multi-clip pipeline (confirmed 2026-04-10)

When the source ad is longer than 15s, use this hybrid i2v→v2v chaining pattern for visual continuity:

Clip 1: i2v mode
  - referenceImages: [product image]  ← establishes brand fidelity
  - audioEnabled: true (if speech)
  - Generate → poll → download output

Clip 2: v2v mode
  - referenceVideos: [clip 1 output]  ← inherits hands, surface, lighting, product
  - NO referenceImages (mutually exclusive)
  - audioEnabled: true (if speech)
  - Upload clip 1 output via fresh presigned URL
  - Generate → poll → download output

Clip 3: v2v mode
  - referenceVideos: [clip 2 output]  ← chain from MOST RECENT clip, not clip 1
  - Upload clip 2 output via fresh presigned URL
  - Generate → poll → download output

...continue for clips 4+

Critical rules for chaining:

1. Always chain from the most recent clip — do not reuse earlier uploads. Presigned URLs expire and stale uploads may fail silently.
2. Upload each clip output fresh via POST /v1/file-upload/get-presigned-url immediately before using it as a reference. Do not reuse filePath values from previous uploads.
3. Wait for each clip to reach generated status before uploading it as a reference for the next clip. Do not fire clips in parallel — they must be sequential.
4. Clip 1 uses i2v for brand fidelity (product image as reference). All subsequent clips use v2v (previous clip as reference) for visual continuity.
5. After all clips are generated, stitch with ffmpeg: ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4 (use absolute paths in the list file).

Why chaining works: Seedance v2v inherits the visual style, hands, surface, lighting, and product appearance from the reference video. By chaining clip N → clip N+1, each subsequent clip maintains continuity with the one before it. The first clip's i2v reference image establishes the product identity; v2v propagates it through the series.

Cost note: Clip 1 costs the i2v rate (~0.06/sec, 0.9 cr at 15s). Clips 2+ cost the v2v rate (~0.1/sec, 1.5 cr at 15s). A 3-clip series costs ~0.9 + 1.5 + 1.5 = ~3.9 credits total.

Important constraints to check:

• referenceImages and referenceVideos are mutually exclusive — pick one per call
• v2v with human-containing reference videos → content checker rejection (credits burned)
• audioEnabled: true + referenceImages may 500 (intermittent server regression) — sanity probe first
• referenceVideos count > 1 fails — only 1 ref video works
• If using v2v: only use product-only/abstract/hands-only videos (no faces)
• Hands-only clips (no face visible) pass the v2v content checker — confirmed 2026-04-10

Tell the user which mode you're using and why.

Step 6: Adapt for user's product

This is the creative core. Using the analysis from step 3:

Dialogue adaptation (if source has speech):

• Keep the same conversational pattern: if the source uses a question hook, use a question hook. If it uses filler words ("like," "okay so"), keep filler words.
• Keep the same number of spoken lines and same silent beat placement
• Keep the same energy arc (excited → calm, or flat, or building)
• Replace product-specific references with the user's product name, features, and claims
• Match the word count of each line closely (±3 words per beat) to preserve pacing
• Read the adapted dialogue out loud at natural pace — it must fit the target duration

Visual adaptation:

• Keep the analyzed camera work, framing per beat, and edit style
• Replace the product description with the user's product (physical appearance, colors, materials, label details)
• Keep the setting, lighting, and atmosphere
• Keep the person description (or adapt if user specifies a different persona)
• Keep the technical flaw cues (phone quality, mic type, lighting imperfections)

Prompt composition:

• Read [seedance-2.md](../prompt-library/seedance-2.md) for platform rules before composing
• Read the closest matching style template (e.g., [seedance-2-ugc.md](../prompt-library/seedance-2-ugc.md) for UGC-style sources) for structural guidance
• Follow the Subject + Action + Camera + Style + Constraints order
• Stay within 100–260 words (Seedance sweet spot)
• Include @(img1) token if user provided a product image
• Add consistency anchors: "The product from @(img1) must remain visually unchanged in every shot"
• Add pacing cues in the tone direction paragraph
• Use timestamps [00:00], [00:04], etc. for multi-beat sequences
• No forbidden words: cinematic, professional, stunning, 8k, studio, perfect

Duration selection:

• If source ≤ 15s: match source duration (or round to nearest second in 4–15 range)
• If source > 15s: split into clips, each ≤ 15s
• Use dialogue word count to validate (see main SKILL.md duration table: ~2.5 words/sec)

Step 7: Dialogue confirmation gate

MANDATORY for any clone with spoken dialogue. Follow the exact format from the main SKILL.md:

📝 Dialogue script (please confirm before I generate)

  1. [HOOK]    "adapted line matching original pattern"
  2. [SHOW]    "adapted feature call-out for user's product"
  3. [DEMO]    (silent beat — physical demonstration, no dialogue)
  4. [VERDICT] "adapted closing line / CTA"

Total spoken words: ~N  |  Target duration: Xs  |  Fits at natural pace: ✅/❌

Approve this dialogue? (yes / edit / rewrite)

Rules:

• This gate is separate from the credit cost confirmation — both must be satisfied
• Never assume approval from earlier confirmations (tone, analysis, credit cost)
• If user says "edit" or proposes changes, revise and re-present until approved
• Skip ONLY if the source video is entirely silent (no speech detected in step 2)

Step 8: Audio decision

Ask the user:

1. Enable audio output? (audioEnabled: true / false)

• Default to true if source video has speech
• Default to false if source video is silent

1. Supply reference audio for voice cloning?

• Offer to extract the source video's audio and use it as referenceAudios
• Or user can provide their own voice clip
• Upload via presigned URL if provided

1. Sanity probe (if using audioEnabled: true + referenceImages):

• This combo has a known regression that returns 500
• Before the full call, fire a minimal test to check if the regression is still active
• If still broken, offer fallbacks:
• Drop audio (audioEnabled: false)
• Drop reference image (text-only, lose brand fidelity)
• Use v2v workaround (generate silent i2v first, then v2v with audio on top)

Step 9: Credit cost estimation

Follow the main SKILL.md's mandatory estimation flow:

1. Check logs/arcads-api.jsonl for matching model + similar config
2. Fall back to MASTER_CONTEXT.md rate table
3. For multi-clip: show per-clip and total
4. Present with source citation and estimate-only disclosure:

Estimated credit cost:
  Seedance 2.0 (15s i2v) × 1 clip × 1 variation = ~0.9 credits
    (from logs/arcads-api.jsonl 2026-04-09)
  ─────────────────────────────────────
  Estimated total: ~0.9 credits

  ⚠️ Estimate only — confirm exact cost in the Arcads platform.
  Proceed? (yes/no)

Do NOT generate until the user confirms.

Step 10: Session setup and upload

Follow the main SKILL.md session folder checklist:

1. GET /v1/products → resolve productId (default from MASTER_CONTEXT.md)
2. Check for existing "Arcads API - YYYY-MM-DD" folder → create if missing
3. Create project inside the folder → store projectId
4. Upload references via POST /v1/file-upload/get-presigned-url:

• Product image → fileType: "image/jpeg" → auto-upscale if longest side /tmp/stitch-list.txt

ffmpeg -y -f concat -safe 0 -i /tmp/stitch-list.txt -c copy stitched-output.mp4 ```

• Provide both stitched file and individual clips

1. Show credit summary (total creditsCharged across all clips/variations)

Seedance 2.0 constraints (quick reference)

Check [reference.md](../../reference.md) for full details. These are the ones most likely to bite during clone-ad:

| Constraint | Impact | |-----------|--------| | referenceVideos + referenceImages mutually exclusive | Cannot combine in same request (500) | | v2v with human faces in reference video | Content checker rejects, credits still charged | | audioEnabled: true + referenceImages regression | May return 500 — sanity probe first | | referenceVideos count > 1 fails | Only 1 reference video accepted despite docs saying 3 | | Content check bills before checking | Credits charged at create time, not refunded on rejection | | endFrame non-functional on Seedance 2.0 | Do not use | | Prompt length | 100–260 words (Seedance sweet spot) | | Duration | 4–15 seconds (continuous integer) | | Aspect ratio | 9:16 or 16:9 only (no 1:1) | | Forbidden words | cinematic, professional, s

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: krusemediallc
• Source: krusemediallc/arcads-claude-code
• License: MIT

Install and usage instructions live in the source repository linked above.