douyin-style-profiler

Extracts 17-dimension style DNA reports from Douyin video transcripts and aggregates them into production templates.

8.5k|1.4k|Updated Apr 24, 2024
One-click install
npx skills add https://github.com/TeamWiseFlow/wiseflow --skill douyin-style-profiler
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: douyin-style-profiler
Source: https://github.com/TeamWiseFlow/wiseflow/tree/main/crews/main/skills/expert-douyin/tools/douyin-style-profiler
Command: npx skills add https://github.com/TeamWiseFlow/wiseflow --skill douyin-style-profiler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Replicating the style of successful Douyin (TikTok China) videos usually relies on vague intuition. This Skill turns reference videos into a structured, evidence-based style system: each video transcript becomes a 17-dimension DNA report, reports are aggregated into a weighted DNA document, and the document is compiled into an executable production template for new content.

Core Features & Use Cases

  • Single-video DNA reports: Generate a 17-dimension analysis scaffold (hook, speech rhythm, shot language, cover frame, interaction design, etc.) from a transcript file, with statistics on sentence length, question density, person usage, and speech rate.
  • Weighted aggregation: Merge multiple reports into a DNA document using per-sample weights and focus restrictions, distinguishing high-frequency patterns from outliers.
  • Production template derivation: Produce a seven-part template (topic, title/cover, hook, development, turn, close, CTA) directly executable during content creation, with incremental updates as new samples arrive.
  • Use Case: Collect transcripts from 10 videos of a benchmark Douyin account, run report on each, then build to obtain a DNA document and template that guide your own scriptwriting and cover generation.

Quick Start

Ask the agent to run douyin-style-profiler report on a prepared transcript file with a DNA ID and sample ID, then run build to aggregate all reports into the DNA document and production template.

Frequently Asked Questions about douyin-style-profiler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze the style of a Douyin video?

Prepare the video's spoken transcript as a .md or .txt file, then run the report command with a DNA ID and sample ID. The script generates a 17-dimension scaffold with statistics, and the agent fills in per-dimension conclusions by re-reading the transcript.

How to aggregate multiple video analyses into one style guide?

Run the build command with a DNA ID after generating individual reports. It reads all reports in the DNA directory, computes weighted statistics, and outputs a DNA document plus a production template with hook, development, turn, close, and CTA sections.

Can I use video files or Douyin links directly as input?

No. The tool only accepts transcript text files (.md or .txt) and local cover images (.jpg/.png/.webp/.gif). Video files and URLs must be transcribed first, for example via an ASR-based teardown tool.

What are the weight and focus parameters used for?

Weight increases a sample's influence during aggregation, while focus restricts a report to specific dimensions such as hook or speech-rhythm. This lets high-value samples shape the DNA document without affecting unrelated dimensions.

Does the tool support incremental updates when new videos are added?

Yes. The update command merges a new report with the existing DNA document and template, recomputes weighted statistics, and preserves prior agent-written conclusions for review and revision.

What are the limitations of the automated statistics?

Statistics cover sentence length, question and pronoun density, exclamation frequency, and speech rate, but they only serve as aggregation evidence. Semantic judgments for all 17 dimensions must be made by the agent re-reading transcripts, and cover analysis requires a vision model on local images.