yyl-benchmark-breakdown

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Original

English
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Translation

Chinese

YYL 对标账号拆解 Skill

YYL Benchmark Account Teardown Skill

做一件事:给一个链接,把对方的内容拆到能复用的程度。
产出固定三件套(见
references/output-template.md
):
  1. 可复用爆款公式 —— 提炼成能直接套用的选题套路 + 结构模板 + 视觉公式。
  2. 画面 + 口播逐段拆解 —— 时间轴对齐,逐段标注画面/口播/钩子/节奏/转折/CTA + 两者协同方式。
  3. 人设与内容定位 —— 人设标签、视觉符号、内容矩阵、差异化打法。
这是分析型 skill,产出 markdown 报告,不产图。要做封面用
yyl-video-thumbnail
,要做小红书图文用
yuyile-social-card-skill
。
Do one thing: Give it a link and break the target’s content down to a reusable level.
The output is a fixed set of three deliverables (see
references/output-template.md
):
  1. Reusable Viral-Content Formula — distill it into directly applicable topic patterns + a structural template + a visual formula.
  2. Segment-by-Segment Breakdown of the Visuals + Voiceover — align them on a timeline and label the visuals/voiceover/hook/pacing/transitions/CTA for each segment + how the two work together.
  3. Persona and Content Positioning — persona tags, visual symbols, content matrix, differentiation strategy.
This is an analytical skill that produces Markdown reports, not images. To create a cover, use
yyl-video-thumbnail
. For Xiaohongshu image-and-text posts, use
yuyile-social-card-skill
.

工作流(前置清单通过后自动开干)

Workflow (automatically starts after the prerequisite checklist passes)

  1. 前置清单(每次开工先跑,但不要反复打扰用户):
    • 跑
      bash scripts/check-deps.sh
      。
    • 如果自检失败:不要开始拆解;先帮用户补环境。按自检输出说明缺什么、为什么需要、下一步命令是什么。
      • Docker 未安装/未启动 → 说明 Docker 是本地下载 API 的运行环境,引导用户安装并打开 Docker Desktop。
      • Douyin_TikTok_Download_API 或 XHS-Downloader API 不可达 → Docker 可用时运行
        bash scripts/bootstrap-local-apis.sh
        ,再重跑
        bash scripts/check-deps.sh
        。
      • ffmpeg / whisper 缺失 → 按
        INSTALL.md
        给出安装命令,装好再重跑自检。
    • 如果必需依赖全部通过:不要问用户确认,直接进入第 1 步。
    • TikHub 是可选项:首次 setup 或目标平台需要 TikHub(YouTube/快手/海外平台/本地 API 失败)且没有
      TIKHUB_API_KEY
      时,给用户两个选择:「现在配置」或「先跳过」。跳过后继续处理本地 API 能覆盖的平台;不要因为 TikHub 缺失阻塞抖音/B站/TikTok/小红书单条。
    前置清单:
    • 必需:Docker、Douyin_TikTok_Download_API、XHS-Downloader API、ffmpeg、openai-whisper。
    • 可选:TikHub API key、Jina Reader key。
  2. 识别:从 URL 判断平台 + 粒度(单条/账号)。规则见
    references/fetch-playbook.md
    。
    • 拿不准粒度就先按单条处理,抓到页面再修正。
  3. 准备证据包(优先跑自动流程):
    • 先跑
      bash scripts/prepare-assets.sh "<url>"
      。
    • 它会自动创建工作目录,保存
      metadata.json
      、下载视频/图片、转写
      audio.txt
      、抽帧并生成
      sheet_*.jpg
      /
      xhs_sheet_*.jpg
      。
    • 跑完先读工作目录里的
      manifest.txt
      ,回显一句「我抓到了什么」(平台/字段/缺什么),再进入拆解。
  4. 取数回退(自动流程失败时继续走,不要在第一级失败就停)。细节、端点、命令见
    references/fetch-playbook.md
    。
    • ① 平台本地 API(主力):
      • 抖音 / TikTok / Bilibili → Douyin_TikTok_Download_API:
        curl http://localhost:80/api/hybrid/video_data?url=...
      • 小红书单条笔记 → XHS-Downloader:
        POST ${XHS_API_BASE:-http://127.0.0.1:5556}/xhs/detail
        。
        download:true
        会下载图文/视频文件到
        ${XHS_DOWNLOAD_DIR:-$HOME/.xhs-downloader/Volume/Download}
        。
    • ② TikHub(备用):YouTube、快手、海外平台、小红书本地 API 失败时用。需要
      TIKHUB_API_KEY
      环境变量。
    • ③ Jina Reader:公众号文章、普通网页、纯图文兜底,
      curl https://r.jina.ai/<url>
      。
    • ④ 引导粘贴:还拿不到 → 让用户发文案/字幕/截图,不报错。
  5. 短视频必做:下载 + 转写口播 + 抽视觉帧(三样都缺不可)
    • 抖音 / B站 / TikTok / YouTube 单条 → 用
      download_addr
      下视频 →
      ffmpeg
      抽音轨 →
      whisper --model medium --language Chinese
      转写 →
      ffmpeg
      场景检测 + 节奏采样 + 拼 contact sheet。
    • 小红书视频笔记 → XHS-Downloader 先下载 mp4 → 复用同一套
      ffmpeg
      +
      whisper
      + contact sheet 流程。
    • 抽完帧用 Read 工具把
      /tmp/bm/sheet_*.jpg
      喂给自己看
      ——多模态读图是视觉拆解的核心证据。
    • 小红书图文笔记 → 用 XHS-Downloader 下载图片,按图片序号拼 contact sheet,把图序当作"视觉脚本"看;没有口播就明确标「无口播」,不硬编。
    • 公众号文章 → 跳过下载/转写,但正文和配图都要作为证据。
    • 命令模板见
      references/fetch-playbook.md
      第 3 节。
  6. 拆解:按通用四层 + 视觉七维 + 平台特化分析。
    • 脚本/结构/选题/转化 →
      references/breakdown-framework.md
    • 画面层(视觉钩子/景别/切点节奏/字幕/视觉符号/色调/场景调度)→
      references/visual-framework.md
    • 单条 → 深拆这一条,画面与口播必须时间轴对齐(看协同/互补/错位/预告/强调)。
    • 账号 → 先抓 5–15 条样本,找重复出现的规律(选题、结构、视觉符号、发布、互动),再挑 1–2 条代表作深拆。
  7. 输出 + 存档:按
    references/output-template.md
    生成报告,同时存一份到
    benchmarks/<平台>-<账号名>/<日期>-<标题slug>.md
    (目录不存在就建)。 存档是第二阶段「长期对标库」的地基,每次都存。
    • 短视频报告末尾必须附
      ## 完整文字转写稿
      ,逐字粘贴
      audio.txt
      的完整内容;不能只给摘要。
    • 图文/网页报告末尾必须附完整正文;如果确实无口播,明确写「无口播」。
  8. 收尾:交付前检查报告末尾是否已经附完整转写稿/正文,再给「这条/这个账号最值得我抄的 3 个点」+ 一句可执行的下一步建议。
  1. Prerequisite checklist (run this first every time, but do not repeatedly bother the user):
    • Run
      bash scripts/check-deps.sh
      .
    • If the self-check fails: do not start the teardown; first help the user prepare the environment. Based on the self-check output, explain what is missing, why it is needed, and what the next command is.
      • Docker is not installed or running → explain that Docker is the runtime environment for the local download APIs, and guide the user to install and open Docker Desktop.
      • Douyin_TikTok_Download_API or XHS-Downloader API is unreachable → if Docker is available, run
        bash scripts/bootstrap-local-apis.sh
        , then rerun
        bash scripts/check-deps.sh
        .
      • ffmpeg / whisper is missing → provide the installation commands from
        INSTALL.md
        , install them, and rerun the self-check.
    • If all required dependencies pass: do not ask the user for confirmation; proceed directly to Step 1.
    • TikHub is optional: during initial setup, or when the target platform requires TikHub (YouTube/Kuaishou/overseas platforms/local API failure) and
      TIKHUB_API_KEY
      is not set, give the user two options: “Configure now” or “Skip for now.” After skipping, continue with the platforms covered by the local APIs; do not block single posts from Douyin/Bilibili/TikTok/Xiaohongshu because TikHub is missing.
    Prerequisites:
    • Required: Docker, Douyin_TikTok_Download_API, XHS-Downloader API, ffmpeg, openai-whisper.
    • Optional: TikHub API key, Jina Reader key.
  2. Identify: Determine the platform and granularity (single post/account) from the URL. See
    references/fetch-playbook.md
    for the rules.
    • If the granularity is uncertain, initially process it as a single post and correct it after fetching the page.
  3. Prepare the evidence package (prioritize the automated workflow):
    • First run
      bash scripts/prepare-assets.sh "<url>"
      .
    • It automatically creates a working directory, saves
      metadata.json
      , downloads videos/images, transcribes
      audio.txt
      , extracts frames, and generates
      sheet_*.jpg
      /
      xhs_sheet_*.jpg
      .
    • After it finishes, first read
      manifest.txt
      in the working directory and echo one sentence explaining “What I found” (platform/available fields/missing items), then begin the teardown.
  4. Data-retrieval fallback (continue when the automated workflow fails; do not stop at the first failure). See
    references/fetch-playbook.md
    for details, endpoints, and commands.
    • ① Platform-local APIs (primary):
      • Douyin / TikTok / Bilibili →
        Douyin_TikTok_Download_API
        :
        curl http://localhost:80/api/hybrid/video_data?url=...
      • Single Xiaohongshu post →
        XHS-Downloader
        :
        POST ${XHS_API_BASE:-http://127.0.0.1:5556}/xhs/detail
        .
        download:true
        downloads image-and-text/video files to
        ${XHS_DOWNLOAD_DIR:-$HOME/.xhs-downloader/Volume/Download}
        .
    • ② TikHub (fallback): Use for YouTube, Kuaishou, overseas platforms, or when the local Xiaohongshu API fails. Requires the
      TIKHUB_API_KEY
      environment variable.
    • ③ Jina Reader: Use for WeChat Official Account articles, regular webpages, and image-and-text-only content as a fallback:
      curl https://r.jina.ai/<url>
      .
    • ④ Guided paste: If the content still cannot be obtained → ask the user to send the copy/subtitles/screenshots. Do not return an error.
  5. Required for short videos: download + voiceover transcription + visual-frame extraction (all three are mandatory)
    • Douyin / Bilibili / TikTok / YouTube single post → download the video using
      download_addr
      → use
      ffmpeg
      to extract the audio track → transcribe it with
      whisper --model medium --language Chinese
      → use
      ffmpeg
      for scene detection + rhythm sampling + contact-sheet assembly.
    • Xiaohongshu video post → first download the mp4 with XHS-Downloader → reuse the same
      ffmpeg
      +
      whisper
      + contact-sheet workflow.
    • After extracting the frames, use the Read tool to load
      /tmp/bm/sheet_*.jpg
      into your own context
      —multimodal image reading is the core evidence for the visual teardown.
    • Xiaohongshu image-and-text post → download the images with XHS-Downloader, assemble a contact sheet in image order, and treat the image sequence as the “visual script”; if there is no voiceover, explicitly mark “No voiceover” and do not invent one.
    • WeChat Official Account article → skip downloading/transcription, but treat the body text and images as evidence.
    • See Section 3 of
      references/fetch-playbook.md
      for command templates.
  6. Teardown: Apply the common four-layer framework + seven visual dimensions + platform-specific analysis.
    • Script/structure/topic selection/conversion →
      references/breakdown-framework.md
    • Visual layer (visual hook/shot size/cut rhythm/subtitles/visual symbols/color palette/scene blocking) →
      references/visual-framework.md
    • Single post → deeply analyze this post; the visuals and voiceover must be aligned on the timeline (analyze synergy/complementarity/mismatch/foreshadowing/emphasis).
    • Account → first fetch 5–15 samples and identify recurring patterns (topic selection, structure, visual symbols, posting, engagement), then select 1–2 representative posts for a deep teardown.
  7. Output + archive: Generate the report according to
    references/output-template.md
    and save a copy at the same time to
    benchmarks/<platform>-<account-name>/<date>-<title-slug>.md
    (create the directory if it does not exist).
    • Archiving is the foundation of the second phase, the “long-term benchmark library.” Save every result.
    • A short-video report must end with
      ## Full Transcript
      , with the complete contents of
      audio.txt
      pasted verbatim; a summary is not sufficient.
    • An image-and-text/web report must include the complete body text; if there is genuinely no voiceover, clearly state “No voiceover.”
  8. Wrap-up: Before delivery, verify that the report ends with the full transcript/body text, then provide “The 3 things most worth copying from this post/account” + one actionable next-step recommendation.

铁律

Hard Rules

  • 自检通过就自动开干:不要再问用户要不要开始;必须完成下载 → 转写 → 视觉识别 → 爆款拆解 → 存档。不能只停在证据包准备完成。
  • 永不空手而归:逐级回退走完;真抓不到就引导粘贴,绝不只甩一句「抓取失败」。
  • 短视频必转写 + 必读图:caption + 数据不够,口播 + 画面 contact sheet 两个证据都要,缺一拆不全。
  • 报告末尾必须附完整转写稿:短视频必须把
    audio.txt
    完整粘贴到
    ## 完整文字转写稿
    ;图文/网页附完整正文或标注「无口播」。这是交付硬门槛,不能只写摘要、不能漏。
  • 要可复用,不要复述:别复述对方讲了什么/拍了什么,要提炼为什么这么做、我怎么套用。
  • 基于证据:数据、原句来自实际抓取;抓不到的指标标「未获取」,不要编。
  • 第一性:每个结论回到「它解决了观众什么问题 / 戳了什么情绪」,不堆术语。
  • 始终存档到
    benchmarks/
    ,文件名含平台、账号、日期。
  • Start automatically as soon as the self-check passes: Do not ask the user whether to begin. You must complete download → transcription → visual analysis → viral-content teardown → archiving. Do not stop after the evidence package is ready.
  • Never come back empty-handed: Work through every fallback tier. If the content truly cannot be fetched, guide the user to paste it; never respond with nothing more than “Fetch failed.”
  • Short videos require transcription + image reading: Captions + data alone are not enough. Both voiceover and visual contact-sheet evidence are required; without either, the teardown is incomplete.
  • The report must end with the full transcript: For short videos, paste the complete contents of
    audio.txt
    under
    ## Full Transcript
    ; for image-and-text/web content, include the complete body text or state “No voiceover.” This is a hard delivery requirement; do not provide only a summary and do not omit anything.
  • Make it reusable, not a restatement: Do not merely repeat what the target said or filmed; distill why they did it and how to apply it.
  • Evidence-based: Data and original wording must come from the actual fetched content. Mark unavailable metrics as “Not retrieved”; do not fabricate them.
  • First principles: Every conclusion should return to “What problem does it solve for viewers / what emotion does it trigger?” Do not pile on jargon.
  • Always archive to
    benchmarks/
    ; the filename must include the platform, account, and date.

文件

Files

  • INSTALL.md
    —— 依赖安装指南(给人看)+ 常见问题。
  • scripts/check-deps.sh
    —— 一键自检前置依赖,缺什么给装的命令。
  • scripts/bootstrap-local-apis.sh
    —— 一键拉起本地 Douyin / XHS 下载 API;Docker 缺失时给安装引导。
  • scripts/prepare-assets.sh
    —— 自动证据包流程:下载 → 转写 → 抽帧/拼图 → 生成 manifest。
  • references/fetch-playbook.md
    —— ★开工先读:平台/粒度识别表、四级取数回退、下载+转写+抽帧命令、粘贴引导话术。
  • references/breakdown-framework.md
    —— 通用四层拆解 + 各平台特化(脚本/结构层) + 爆款公式提炼法。
  • references/visual-framework.md
    —— ★短视频必读:画面七维拆解 + 视觉/口播时间轴对齐 + 视觉公式提炼法。
  • references/output-template.md
    —— 三件套交付模板(已含画面+口播对齐表) + 存档命名约定。
  • INSTALL.md
    — dependency installation guide (for humans) + common issues.
  • scripts/check-deps.sh
    — one-click prerequisite self-check, with installation commands for anything missing.
  • scripts/bootstrap-local-apis.sh
    — one-click startup for the local Douyin / XHS download APIs, with installation guidance when Docker is missing.
  • scripts/prepare-assets.sh
    — automated evidence-package workflow: download → transcribe → extract frames/assemble contact sheets → generate the manifest.
  • references/fetch-playbook.md
    — ★ Read before starting: platform/granularity identification table, four-tier data-retrieval fallback, download + transcription + frame-extraction commands, and wording for paste guidance.
  • references/breakdown-framework.md
    — common four-layer teardown + platform-specific adaptations (script/structure layers) + method for extracting viral-content formulas.
  • references/visual-framework.md
    — ★ Required for short videos: seven visual dimensions + visual/voiceover timeline alignment + method for extracting visual formulas.
  • references/output-template.md
    — three-part deliverable template (already includes a visuals + voiceover alignment table) + archive naming convention.

第二阶段(暂未实现,已留地基)

Phase Two (not yet implemented, but the foundation is in place)

  • 长期对标账号库:基于
    benchmarks/
    里沉淀的多份拆解,做跨账号/跨时间的规律汇总(谁的哪类选题稳定爆、行业共性、可追踪趋势)。
  • 长视频转写优化:>15 分钟视频自动切片 + 并行转写;或接商业 API(飞书妙记/通义听悟)。
  • 小红书账号级自动化:接 MediaCrawler 自托管,用于账号主页采样、评论与搜索;单条下载已经由 XHS-Downloader 覆盖。
等单链接拆解打磨稳了再做。
  • Long-term benchmark account library: Based on multiple teardowns accumulated in
    benchmarks/
    , summarize patterns across accounts and time (whose topic categories consistently go viral, industry-wide commonalities, and trackable trends).
  • Long-video transcription optimization: Automatically split videos longer than 15 minutes and transcribe them in parallel; or integrate commercial APIs (Feishu Minutes/Tongyi Tingwu).
  • Xiaohongshu account-level automation: Integrate self-hosted MediaCrawler for account-homepage sampling, comments, and search; single-post downloads are already covered by XHS-Downloader.
Wait until the single-link teardown workflow has been polished and stabilized.