v1.0.0 to v1.0.0

---

name: viral-reverse-engineering

description: >-

Use to reverse-engineer why a piece of content went viral (or overperformed) — yours or someone

else's — and extract the repeatable mechanism to apply to your own content. Run when the user says

"why did this go viral," "break down this viral post/video," "reverse engineer," "what made this

work," "analyze this top performer," or wants to learn from viral content. Because an agent usually

can't watch a video from a link, it first sources the observable signal (a structured intake,

transcript, screenshots/key frames, top comments, visible stats — or a fetch/subtitles tool where

available) and never fabricates what it can't see. Reads brand-profile and audience first,

deconstructs the piece layer by layer (hook, emotional driver, structure, packaging, share-trigger,

distribution), isolates the real driver from incidental features, runs a replicability check

(survivorship bias, luck, account size, one-time moments), then extracts the transferable principle

and applies it to the user's niche — handing execution to the content skills. Reverse-engineers the

mechanism, never a copy; flags when virality isn't replicable; uses visible/native signals (no

metadata:

version: 1.0.0

license: MIT

---

# 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.

## 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.