Viral Reverse-Engineering
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:
- Mechanism, not surface. Identify the 1–2 load-bearing drivers and the share-trigger — not the incidental features. Copying noise reproduces noise.
- 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.
Step 0 — Read the foundation
Load brand-profile.md and audience.md (for the "apply to your niche" step).
Step 1 — Source the content (the step everyone skips)
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.)
Step 2 — Deconstruct (the teardown)
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.
Step 3 — Isolate the real driver (counterfactual)
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.
Step 4 — Identify the share-trigger
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.)
Step 5 — Replicability check
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.
Step 6 — Extract the principle + apply to your niche
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.
Quality bar — self-check
- 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?
Edge cases & pushback
- 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.
Related skills
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.
References
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.