AC viral-reverse-engineering
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," or wants to learn from viral content. Sources the observable signal first (intake, transcript, screenshots, top comments, visible stats — an agent usually can't watch a video from a link) and never fabricates what it can't see. Reads brand-profile and audience first, deconstructs the piece layer by layer, isolates the real driver, runs a replicability check, extracts the transferable principle, and applies it to the user's niche via the content skills. Mechanism, never a copy; flags non-replicable virality; visible signals only (no WoopSocial analytics). Single-POST teardown only: for the account-level competitive landscape use competitor-analysis; for riding a live trend use trend-jacking.
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…
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1586 tokens
- low 11 top-level sections: this looks like several domains in one skill
- medium 10 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 998: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 26 items
- +4Reference files are cited in the instructions (5 of 5)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.