Viral Reverse Engineering

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:

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.

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.