Douyin industry video
Skill fengziliang43-cmyk/douyin-industry-video-skill/.agents/skills/douyin-industry-video
Extract subtitles and metadata from Douyin or short-video links, then analyze investment or industry-chain claims in Chinese. Use when the user shares a Douyin/TikTok-style video link or downloaded video and asks to get complete subtitles, summarize the video, analyze AI/semiconductor/memory/order logic, identify who pays, judge cycle stage, or turn the workflow into reusable research.From its SKILL.md
npx -y skills add fengziliang43-cmyk/douyin-industry-video-skill --skill douyin-industry-videoAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
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SKILL.md
3.7 KB, 767 tokens by cl100k_base, as published. Nobody here has run it
Douyin Industry Video
Use this skill to turn a short-video link into a verifiable transcript and an industry-research answer. Default to Chinese replies.
Workflow
- Read local project context first when available:
- Follow the current workspace's
AGENTS.md,CLAUDE.md, or equivalent instructions. - If the user maintains a research vault or durable notes, read the relevant investing, industry-chain, AI hardware, memory, orders, or "who pays" notes before analysis.
- If no durable context exists, continue with the built-in framework in
references/analysis-framework.md.
- Follow the current workspace's
- Fetch the video:
- For a Douyin URL, run
scripts/fetch_douyin_video.py <url> --out-dir work/video. - If the user already provided a local video, skip fetch and use that file.
- For a Douyin URL, run
- Get subtitles in this order:
- Use official page metadata or subtitle fields if present.
- If there are burned-in subtitles, run
scripts/ocr_burned_subtitles.swift <video.mp4> <raw.jsonl> 1. - Clean OCR with
scripts/clean_ocr_subtitles.py <raw.jsonl> <transcript.md> --json <transcript.json>. - If OCR quality is weak and an OpenAI audio key or local
whisperCLI is available, transcribe audio and cross-check. Never print keys or save secrets.
- State transcript confidence:
- Say whether the transcript is official, audio-transcribed, or OCR-derived.
- For OCR, mark it as machine extracted from burned subtitles and note likely OCR errors.
- Analyze the video:
- Separate
video claim -> evidence -> inference -> risk. - For industry-chain topics, use
天道产业链版completeness: reality check, chain map, true bottleneck, company mapping, revenue/profit elasticity, event energy, falsification, tracking metrics, unresolved checks. - For "AI chain all have orders / who pays" claims, apply the sponsor chain:
hyperscaler / AI cloud capex -> GPU / ASIC -> TSMC / advanced packaging -> HBM / server DRAM / SSD -> equipment / materials / power / optical interconnect.
- Separate
- Memory closeout:
- If a reusable framework, trigger, or follow-up is created, update the user's configured durable memory or project notes when allowed.
- Do not store raw chat logs, secrets, account details, exact personal holdings, or irrelevant one-off content.
Practical Rules
- Do not pretend to have watched or transcribed a video if only metadata was available.
- Prefer direct evidence: transcript, official filings, earnings calls, company press releases, and current market data.
- Browse for current facts when the answer depends on recent financials, capex guidance, orders, prices, or stock-market moves.
- For investment answers, do not provide buy/sell instructions. Separate industry quality, cycle stage, valuation/timing, and position risk.
- Give numeric event energy when relevant, for example
短线事件能量 86/100,中期兑现能量 74/100.
Script Notes
fetch_douyin_video.pyuses mobile user-agent HTML parsing and downloads the reflow video when Douyin exposesplay_addr.ocr_burned_subtitles.swiftuses macOS AVFoundation and Vision. It requires no global install but only works on macOS.clean_ocr_subtitles.pyfilters the subtitle band, deduplicates repeated OCR frames, and outputs Markdown plus optional JSON.
What ships with it: 5 files
12.0 KB alongside SKILL.md, 2 of them executable
agents/
- openai.yaml263 B
references/
- analysis-framework.md1.1 KB
scripts/
- clean_ocr_subtitles.pyruns3.8 KB
- fetch_douyin_video.pyruns3.1 KB
- ocr_burned_subtitles.swift3.7 KB