creatok-analyze-video

Category: Business & Operations | Uploader: EchoSell | Downloads: 0 | Version: v1.0（Latest）

This skill should be used when the user asks to analyze a TikTok video, break down a viral TikTok, understand why a TikTok script works, view the original script, view the original storyboard, study a selling video, review a TikTok hook, or adapt a reference TikTok into their own version. Analyzes TikTok videos through CreatOK's remote analyze interface and helps creators or sellers understand hook, structure, selling logic, proof, CTA, original script, and original storyboard in simple business language.

Changelog: Source: GitHub https://github.com/EchoSell/creatok-skills

Directory Structure

Current level: skills/creatok-analyze-video/

📁 lib/
- 📄 analyze-video.js 2.2 KB
- 📄 artifacts.js 1.0 KB
- 📄 config.js 334 B
- 📄 creatok-client.js 2.3 KB
📁 references/
- 📄 common-rules.md 1.3 KB
- 📄 contracts.md 1.2 KB
📁 scripts/
- 📄 run.js 1000 B
📄 SKILL.md 7.9 KB

SKILL.md

---
name: creatok-analyze-video
version: "1.0.0"
description: "This skill should be used when the user asks to analyze a TikTok video, break down a viral TikTok, understand why a TikTok script works, view the original script, view the original storyboard, study a selling video, review a TikTok hook, or adapt a reference TikTok into their own version. Analyzes TikTok videos through CreatOK's remote analyze interface and helps creators or sellers understand hook, structure, selling logic, proof, CTA, original script, and original storyboard in simple business language."
license: Internal
compatibility: "Claude Code ≥1.0, OpenClaw skills, ClawHub-compatible installers. Requires network access to CreatOK Open Skills API. No local ffmpeg or vision setup required."
metadata:
  openclaw:
    requires:
      env: []
      bins:
        - node
    primaryEnv: CREATOK_API_KEY
  author: creatok
  version: "1.0.0"
  geo-relevance: "low"
  tags:
    - tiktok
    - tiktok-analysis
    - video-analysis
    - selling-video
    - script-analysis
    - storyboard
    - hook-analysis
    - creator-workflow
    - seller-workflow
    - viral-video
    - remix-prep
    - ecommerce
    - ugc
  triggers:
    - "analyze this TikTok video"
    - "analyze this TikTok"
    - "analyze this video"
    - "break down this TikTok"
    - "tiktok video analysis"
    - "why does this TikTok work"
    - "show me the original script"
    - "show me the original storyboard"
    - "analyze this selling video"
    - "analyze this hook"
    - "study this viral video"
    - "help me adapt this video"
    - "show me why this works"
    - "what makes this convert"
    - "show me the script and help me rewrite it"
---

# analyze-video

## Constraints

- Platform: **TikTok only**.
- Analyze source: **CreatOK `/api/open/skills/analyze`**. The remote service is responsible for transcript and vision extraction.
- The model's final user-facing response should match the user's input language, default **English**.
- Avoid technical wording in the user-facing reply unless the user explicitly needs details for debugging or to share with a developer.
- Follow shared guidance in `./references/common-rules.md`.
- Input: **TikTok URL**.
- Artifacts must be written under `analyze-video/.artifacts/<run_id>/...`.

## What to produce (minimum)

Create:

- `outputs/result.json` (machine-readable, see `./references/contracts.md`)

The script gathers structured source data returned by CreatOK:

- transcript segments
- video metadata
- normalized vision result
- remote response text and suggestions

## Analysis Focus

The model should read `outputs/result.json` and produce the final user-facing analysis in the conversation.
Before deciding how to explain the result, the model should first infer what kind of TikTok video this is.
This classification is mainly for better guidance and analysis focus; it should not feel like a rigid taxonomy to the user.
Useful internal categories include:

- selling talking-head / direct pitch
- pain-point to solution
- product demo
- before / after
- review / comparison
- listicle / recommendation
- emotional or surprise hook
- non-selling content such as pet, entertainment, lifestyle, or story content

The model does not need to expose the category label unless it clearly helps the user.

## Analysis Angles

The model can infer and explain items such as:

- hook / value / proof / CTA
- highlights with timestamps
- storyboard / reusable template
- final written analysis or recommendations
- why the video can or cannot go viral from a short-form content operations perspective
- how the video works from a selling conversion perspective, including script, cover, audience, and conversion logic

Two especially useful framing options for the final user-facing analysis are:

- explain why the video can or cannot become a strong short-form performer from an operator's point of view
- break down the script, cover, audience, and conversion logic from a selling and transaction point of view

The analysis emphasis should follow the inferred video type:

- for selling videos, focus on conversion structure, selling-point order, proof, trust-building, and CTA
- for product demos, focus on what is shown first, how the product is demonstrated, and what makes the demo persuasive
- for before / after videos, focus on contrast strength, believability, and payoff timing
- for review / comparison videos, focus on credibility, differentiation, and decision-making signals
- for non-selling content, focus on hook, pacing, emotional pull, and what structure can be reused without forcing a selling analysis

## Output Preferences

- When the user asks for the original script, the default format should be a timestamped line-by-line script.
- Prefer a clean readable structure such as one spoken line per row with its corresponding time range.
- Keep the final response easy for creators and sellers to scan and reuse.

## Next-Step Handoff

After presenting the analysis, the model should naturally guide the user into the next step.
Prefer a light transition such as:

- show the original script extracted from the reference
- show the original storyboard / scene breakdown from the reference
- break it down into reusable templates for storyboards and sales video structures
- rewrite this into a version for the user's own product
- turn the analyzed direction into an AI-generation-ready version

The model should keep this handoff flexible and concise rather than forcing a rigid workflow.
The model should prefer prompts that naturally invite the user's next reply to match `creatok-recreate-video`, for example:

- "I can rewrite this into a version for your product."
- "I can make you a similar version with a different angle."
- "I can keep the structure and rewrite the script for your offer."

The next-step options should also reflect the inferred video type:

- for selling videos, prioritize viewing the original script, viewing the original storyboard, adapting it to the user's own product, or making a differentiated version
- for non-selling content, prioritize viewing the original script, viewing the original storyboard, or adapting the idea to the user's own topic

Unless the user explicitly asks for a live-action shoot version, the model should treat recreation and follow-up generation as AI-generated video work by default.
The default path is to help the user move toward an AI-generation-ready script or brief.
After giving a useful AI-oriented version, the model may optionally ask whether the user also wants a live-action shoot version.

If the reference appears to be a product-selling video and the user wants to recreate it, the model should first collect the user's own product context before drafting the recreated script.
Ask only for the highest-impact details first, such as:

- product name
- core selling points
- product images or reference materials if available
- price or offer details if they matter to the hook or CTA

If important details are still missing, the model should fill gaps through short follow-up questions step by step instead of requesting a large information dump up front.
The model should not ask for a long form, a detailed brief, or a large batch of requirements before showing useful progress.

## Workflow

1. **Create run folder**

- Use user-provided `run_id`
- Create `analyze-video/.artifacts/<run_id>/{input,transcript,vision,outputs,logs}`

2. **Call remote analyze**

- Call CreatOK: `POST /api/open/skills/analyze`
- Persist:
  - `input/video_details.json`
  - `transcript/transcript.json` (segments)
  - `transcript/transcript.txt`
  - `vision/vision.json`

3. **Write artifacts**

- `outputs/result.json`

## Notes

- Keep it deterministic and portable: write source data artifacts and let the model analyze them in the conversation.
- Favor momentum after the analysis. The default next step is to help the user view the original materials or move toward recreation / remix.
- For selling-video recreation, gather a small set of key product details first, then refine through lightweight follow-up questions only when needed.

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