Registry indexed
Clone an existing video ad for a different product or offer. Analyzes the source video's style, pacing, camera work, dialogue, and tone, then adapts and generates a new Seedance 2.0 video customized for the user's product. End-to-end workflow: input video → analysis → adapted pro
Clone an existing video ad for a different product or offer. Analyzes the source video's style, pacing, camera work, dialogue, and tone, then adapts and generates a new Seedance 2.0 video customized for the user's product. End-to-end workflow: input video → analysis → adapted prompt → generation → delivery. Use when someone says "clone this ad", "make this ad but for my product", "recreate this video for my brand", or provides a video ad and a product image asking for a similar video.
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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:
prompt-library/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.
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.
Reuse the analyze-video extraction script — do NOT duplicate it.
bash "skills/arcads-external-api/prompting/analyze-video/scripts/extract-frames.sh" \
"<source_video_path>" "/tmp/clone-ad-analysis" <num_frames>
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.jpgaudio.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.
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:
result["segments"])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.
Read ALL extracted frames visually. For each frame, note:
Structure and pacing:
Camera and framing:
Edit style:
Dialogue and script structure:
Tone and energy:
Lighting and technical quality:
Product references:
What makes this ad distinctive (2–3 defining traits):
Store this analysis internally — it does NOT get saved as a template file.
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.
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
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:
POST /v1/file-upload/get-presigned-url immediately before using it as a reference. Do not reuse filePath values from previous uploads.generated status before uploading it as a reference for the next clip. Do not fire clips in parallel — they must be sequential.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 callaudioEnabled: true + referenceImages may 500 (intermittent server regression) — sanity probe firstreferenceVideos count > 1 fails — only 1 ref video worksTell the user which mode you're using and why.
This is the creative core. Using the analysis from step 3:
Dialogue adaptation (if source has speech):
Visual adaptation:
Prompt composition:
@(img1) token if user provided a product imagename: clone-ad description: > Clone an existing video ad for a different product or offer. Analyzes the source video's style, pacing, camera work, dialogue, and tone, then adapts and generates a new Seedance 2.0 video customized for the user's product. End-to-end workflow: input video → analysis → adapted prompt → generation → delivery. Use when someone says "clone this ad", "make this ad but for my product", "recreate this video for my brand", or provides a video ad and a product image asking for a similar video.
---
name: clone-ad
description: >
Clone an existing video ad for a different product or offer. Analyzes the source
video's style, pacing, camera work, dialogue, and tone, then adapts and generates
a new Seedance 2.0 video customized for the user's product. End-to-end workflow:
input video → analysis → adapted prompt → generation → delivery. Use when someone
says "clone this ad", "make this ad but for my product", "recreate this video for
my brand", or provides a video ad and a product image asking for a similar video.
---
# 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:
```bash
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
bash "skills/arcads-external-api/prompting/analyze-video/scripts/extract-frames.sh" \
"<source_video_path>" "/tmp/clone-ad-analysis" <num_frames>
```
**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.
```python
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 produSkill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
72/100
Strong
Trust
68/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"url": "https://www.openagentskill.com/skills/krusemediallc-clone-ad",
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"score": 76,
"label": "Strong shortlist",
"version": "trust-score-v4",
"install_policy": "block",
"evidence": {
"stars": "1.5K GitHub stars",
"repoActivity": "1.5K stars, 357 forks",
"lastPushed": "2mo since push",
"license": "MIT",
"repository": "https://github.com/krusemediallc/arcads-claude-code/tree/main/skills/arcads-external-api/prompting/clone-ad",
"install": "npx skills add krusemediallc/arcads-claude-code --skill clone-ad",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Strong README/SKILL.md context",
"agentOutcomes": "No agent outcome data yet"
},
"outcome_evidence": {
"total": 0,
"successes": 0,
"failures": 0,
"not_relevant": 0,
"success_rate": null,
"recent_success_rate": null,
"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"research",
"agent-skill"
],
"known_risks": [
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 78,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 72,
"label": "Strong"
},
"supply": {
"track": "Research and knowledge work",
"scenario": "Research agents",
"maintenance": "2mo since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "yanliudesign-mono-color-skill",
"name": "mono-color",
"url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
"stars": 1919,
"install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
"trust_score": 85,
"audit_score": 93
},
{
"slug": "mvanhorn-last30days-skill",
"name": "Last30days Skill",
"url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
"stars": 60956,
"install_command": "",
"trust_score": 94,
"audit_score": 95
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No major risk signals from current metadata",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision."
],
"agent_contract": {
"task_input": "Use clone-ad in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 76/100 Strong shortlist",
"Audit: 78/100 Needs review",
"Safety: 38/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "krusemediallc-clone-ad (clone-ad)",
"install_command": "npx skills add krusemediallc/arcads-claude-code --skill clone-ad",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "krusemediallc-clone-ad",
"task": "Use clone-ad in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/krusemediallc-clone-ad",
"api": "https://www.openagentskill.com/api/agent/skills/krusemediallc-clone-ad",
"audit": "https://www.openagentskill.com/skills/krusemediallc-clone-ad/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=krusemediallc-clone-ad&task=Use%20clone-ad%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20clone-ad%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20clone-ad%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/krusemediallc-clone-ad/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/krusemediallc-clone-ad"
}
}Listing source
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Sandbox only
Audit
78/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.