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Viral Reverse-Engineering

爆款内容逆向分析

Most "learn from viral content" advice produces flops, because people copy the surface (the same sound, topic, format) instead of the mechanism (the load-bearing hook, the emotional trigger, the share driver). This skill does the opposite: it tears a piece down, finds what actually drove it, checks whether that's even replicable, and turns it into a principle you can apply in your own niche.
Two commitments:
  1. Mechanism, not surface. Identify the 1–2 load-bearing drivers and the share-trigger — not the incidental features. Copying noise reproduces noise.
  2. Honest about luck and survivorship. A lot of virality is account size, timing, a one-time moment, or plain randomness. When success isn't replicable, say so — a false formula is worse than none.
大多数“向爆款内容学习”的建议最终都收效甚微，因为人们只模仿表面元素（相同的音效、主题、形式），而非核心机制（起关键作用的钩子、情感触发点、分享动因）。本技能则反其道而行之：它会拆解内容，找出真正驱动其爆火的因素，验证该因素是否可复制，并将其转化为可应用于你所在细分领域的原则。
两大承诺：
  1. 聚焦机制，而非表面。找出1-2个核心驱动因素和分享触发点——而非次要特征。模仿无用的细节只会产出同样无用的内容。
  2. 坦诚看待运气与幸存者偏差。很多爆款源于账号规模、时机、一次性事件或纯粹的随机性。当成功无法复制时，要如实说明——错误的方法论比没有更糟。

Step 0 — Read the foundation

步骤0 —— 了解基础信息

Load 
brand-profile.md
and 
audience.md
(for the "apply to your niche" step).
加载
brand-profile.md
和
audience.md
（用于“应用到你的细分领域”步骤）。

Step 1 — Source the content (the step everyone skips)

步骤1 —— 获取内容信息（所有人都会跳过的步骤）

You usually can't watch a video from a link — platforms are walled, and a fetch returns metadata at best. So this skill analyzes whatever observable signal is brought in: the user's description, a transcript, screenshots/key frames (multimodal), the top comments, and the visible stats (views/likes/shares/comments, follower count) — or a fetch/subtitles tool where the agent has one. Run the structured intake in 
references/sourcing-the-content.md
: ask for the hook, a play-by-play/transcript, caption + on-screen text, format, stats, creator size, and sound.
The rule: the human (or a transcript/screenshot/tool) is the eyes; the skill is the analyst. Never fabricate frames or lines you weren't given — analyze what's provided and name the gaps. Also: patterns need multiple examples — one viral post is an anecdote. (WoopSocial has no analytics; work from visible/native signals or pasted data.)
通常你无法通过链接观看视频——平台有访问限制，获取链接最多只能得到元数据。因此本技能分析的是所有可观测信号：用户的描述、文字稿、截图/关键帧（多模态）、热门评论以及可见数据（浏览量/点赞/分享/评论数、粉丝数）——或者使用Agent可用的字幕/获取工具。执行
references/sourcing-the-content.md
中的结构化信息收集：询问钩子内容、逐帧/文字稿详情、字幕+屏幕文本、形式、数据、创作者账号规模和音效。
规则：人类（或文字稿/截图/工具）是“眼睛”；技能是“分析师”。绝不编造未提供的画面或台词——仅分析已提供的内容，并说明信息缺口。另外：规律需要多个案例支撑——单条爆款帖子只是个例。（不使用WoopSocial分析工具；仅基于可见/原生信号或粘贴的数据开展工作。）

Step 2 — Deconstruct (the teardown)

步骤2 —— 拆解内容（剖析过程）

Tear down each layer: hook, emotional/share driver, retention structure, format/packaging, topic/angle, share-trigger, distribution factors. One line per layer; don't praise everything. See 
references/deconstruction-framework.md
.
逐层拆解：钩子、情感/分享动因、留存结构、形式/包装、主题/视角、分享触发点、传播因素。每层用一句话描述；不要全盘夸赞。详见
references/deconstruction-framework.md
。

Step 3 — Isolate the real driver (counterfactual)

步骤3 —— 定位真正的驱动因素（反事实验证）

For each notable feature, ask "remove this — does it still pop?" Whatever it can't lose without collapsing is a driver; what it can lose is incidental. Usually only 1–2 layers are load-bearing (typically the hook + the emotional/share trigger). Most bad analysis credits the noise.
针对每个显著特征，问自己：“去掉这个特征，内容还会爆吗？” 去掉后会导致内容失效的就是驱动因素；可去掉的则是次要特征。通常只有1-2层是核心（通常是钩子+情感/分享触发点）。大多数糟糕的分析都会把次要特征当成核心。

Step 4 — Identify the share-trigger

步骤4 —— 识别分享触发点

Virality = shares, so name why people sent it to someone else: identity/self-expression, high-arousal emotion (awe/anger/humor/inspiration), social currency, practical value, relatability, story. A piece with no share-trigger gets views, not virality. See 
references/why-things-spread.md
. (The top comments are the best evidence here — see 
references/sourcing-the-content.md
.)
爆款=分享，所以要明确人们为什么会把它转发给他人：身份/自我表达、高唤醒情绪（敬畏/愤怒/幽默/鼓舞）、社交货币、实用价值、共鸣、故事性。没有分享触发点的内容只会获得浏览量，无法成为爆款。详见
references/why-things-spread.md
。（热门评论是最好的证据——详见
references/sourcing-the-content.md
。）

Step 5 — Replicability check

步骤5 —— 可复制性验证

Screen for confounds before extracting anything: account-size advantage, luck/variance, one-time moments, survivorship bias, sample size. If the success is mostly confound, flag it as non-replicable and don't invent a principle. See 
references/replicability-and-application.md
.
在提取原则前先排查干扰因素：账号规模优势、运气/随机性、一次性事件、幸存者偏差、样本量。如果成功主要源于干扰因素，标记为不可复制，不要编造原则。详见
references/replicability-and-application.md
。

Step 6 — Extract the principle + apply to your niche

步骤6 —— 提取原则并应用到你的细分领域

State the mechanism in one line, translate it to the user's subject (same mechanism, your topic), and hand execution to the content skills (
hook-writer
, 
tiktok-script
, 
reels-script
, 
caption-writer
, 
carousel-writer
) in the brand voice. Output is "the lever is X; here's X applied to you" — never a copy. Build a swipe file of recurring patterns over time.
用一句话阐述核心机制，将其转化为用户所在领域的内容（相同机制，你的主题），并交由内容技能（
hook-writer
、
tiktok-script
、
reels-script
、
caption-writer
、
carousel-writer
）以品牌风格执行。输出内容应为“核心杠杆是X；以下是X在你领域的应用”——绝不照搬原文。长期积累可复用的规律，建立素材库。

Quality bar — self-check

质量标准 —— 自我检查

  • Did I source real input (intake/transcript/screenshots/comments), and not fabricate what I couldn't see — naming the gaps?
  • Did I find the mechanism (1–2 real drivers + the share-trigger), not the surface?
  • Did the counterfactual rule out incidental features?
  • Did I run the replicability check and flag confounds/luck/small-sample honestly?
  • Is the output a principle applied to the user's niche, not a copy?
  • Did I respect the ethics line (inspiration, not plagiarism/IP theft)?
  • Did I use visible/native signals with no analytics claims, and make no virality guarantees?
  • 我是否获取了真实输入（用户提供的信息/文字稿/截图/评论），且未编造无法获取的内容——并说明了信息缺口？
  • 我是否找到了核心机制（1-2个真正的驱动因素+分享触发点），而非表面元素？
  • 我是否通过反事实验证排除了次要特征？
  • 我是否进行了可复制性验证，并坦诚标记了干扰因素/运气/小样本问题？
  • 输出内容是否是应用到用户细分领域的原则，而非照搬原文？
  • 我是否遵守了道德底线（灵感启发，而非抄袭/知识产权盗用）？
  • 我是否仅使用了可见/原生信号，未做出分析工具相关的声明，且未做出爆款保证？

Edge cases & pushback

边缘情况与应对

  • Bare link, nothing else → explain you can't watch the video; run the intake (ask for transcript/screenshots/stats) or use a subtitles/fetch tool if available; don't pretend you saw it.
  • Partial input (transcript only, screenshots only) → analyze what's there, name what you can't assess (e.g., pacing/edit, or the spoken layer).
  • "Copy it exactly with our product" → mechanism + your own substance, not a surface copy (derivative + IP risk).
  • "It was the sound/topic" → counterfactual-test it; usually the hook + trigger were the real lever.
  • Huge-account / one-time virality → flag non-replicable; don't extract a false formula.
  • One example → anecdote, not a pattern; tear down several to find recurring mechanisms.
  • "Guarantee us viral" → no guarantees (luck/distribution); stack the odds via mechanisms.
  • No data to judge "viral" → use visible signals; be clear about the limits.
  • 仅提供链接，无其他信息 → 说明无法观看视频；执行信息收集（索要文字稿/截图/数据），或使用可用的字幕/获取工具；不要假装看过视频。
  • 部分输入（仅文字稿、仅截图） → 分析已提供的内容，说明无法评估的部分（例如节奏/剪辑，或语音层面）。
  • “完全照搬并替换成我们的产品” → 采用核心机制+自有内容，而非表面复制（衍生内容存在知识产权风险）。
  • “是音效/主题让它爆的” → 进行反事实验证；通常钩子+触发点才是真正的核心杠杆。
  • 大号账号/一次性爆款 → 标记为不可复制；不要提取错误的方法论。
  • 单个案例 → 只是个例，而非规律；拆解多个案例才能找出可复用的机制。
  • “保证我们的内容爆火” → 无法保证（运气/传播因素）；只能通过核心机制提高概率。
  • 无数据判断是否“爆款” → 使用可见信号；明确说明局限性。

Related skills

相关技能

  • brand-profile
    , 
    audience-research
    — relevance + the "apply to your niche" step.
  • hook-writer
    — the most common load-bearing driver; 
    trend-jacking
    — overlapping "why it spread."
  • tiktok-script
    , 
    reels-script
    , 
    caption-writer
    , 
    carousel-writer
    — execute the extracted principle.
  • competitor-analysis
    , 
    analytics-and-reporting
    (advisory) — broader performance analysis.
  • brand-profile
    、
    audience-research
    —— 相关性+“应用到你的细分领域”步骤。
  • hook-writer
    —— 最常见的核心驱动因素；
    trend-jacking
    —— 与“传播原因”部分重叠。
  • tiktok-script
    、
    reels-script
    、
    caption-writer
    、
    carousel-writer
    —— 执行提取的原则。
  • competitor-analysis
    、
    analytics-and-reporting
    （咨询类） —— 更广泛的表现分析。

References

参考资料

  • references/sourcing-the-content.md
    — how the content gets into context (intake, transcripts, screenshots, comments, tools) + graceful degradation. Start here.
  • references/deconstruction-framework.md
    — the layer-by-layer teardown + the counterfactual driver test.
  • references/why-things-spread.md
    — the share-trigger psychology (why people share).
  • references/replicability-and-application.md
    — survivorship/luck/sample-size honesty; extract + apply; ethics.
  • references/examples.md
    — worked teardowns, including a non-replicable case.
  • references/sourcing-the-content.md
    —— 内容信息获取方式（用户提供的信息、文字稿、截图、评论、工具）+ 容错处理。从这里开始。
  • references/deconstruction-framework.md
    —— 逐层拆解方法+反事实驱动因素测试。
  • references/why-things-spread.md
    —— 分享触发点心理学（人们为什么分享）。
  • references/replicability-and-application.md
    —— 幸存者偏差/运气/样本量的坦诚处理；提取+应用；道德规范。
  • references/examples.md
    —— 已完成的拆解案例，包括不可复制的案例。