social-post-ab-variants-writer — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited social-post-ab-variants-writer (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Generates two or three distinct social media post variants for the same article or piece of content, each testing a different angle, hook, or emotional register, so you can run a controlled comparison and learn what resonates with your audience.
Required: The headline or title of the piece, a one-paragraph summary of what the article covers, and the platform you are posting to (LinkedIn, X/Twitter, Instagram, Facebook, or Bluesky).
Optional: The primary audience segment (general public, specialists, decision-makers); any constraints on length or tone set by your publication's style guide; the specific hypothesis you want to test (e.g., "curiosity-gap hook vs. direct statement of fact"); any call-to-action wording you want to keep consistent across variants.
Two or three labelled variants, each 40–280 characters depending on the platform, preceded by a one-line label identifying the hook strategy. Each variant is self-contained — ready to paste into a scheduling tool. A brief note at the end explains what outcome would indicate which variant is performing better. No need for the editor to interpret the test themselves.
Article headline: Rising rents are pushing key workers out of the cities that need them most
Summary: A data-led feature showing that nurses, teachers, and paramedics in major metropolitan areas spend an average of 42 percent of their take-home pay on rent, with one in five reporting they have already moved or are planning to move more than 45 minutes from their workplace. Draws on housing cost data from eight cities over five years.
Platform: X (Twitter)
Audience: General public
Hypothesis to test: Does a human-interest framing outperform a data-led framing?
Variant A — Data-led hook Nurses. Teachers. Paramedics. They spend 42% of their salary on rent — and 1 in 5 are already planning to leave. New data from 8 cities shows who's being priced out of the places that need them most. [link]
Testing: Does citing the specific statistic upfront drive more clicks?
Variant B — Human-interest hook "I love my job. I just can't afford to live near it anymore." Key workers are being priced out of the cities they keep running. [link]
Testing: Does a first-person voice and emotional framing outperform a data-led opening?
How to read the results: If Variant A gets more link clicks but fewer likes and replies, the data framing is driving readers to the article but not sparking conversation. If Variant B gets more replies and shares, the human framing is generating emotional resonance. Run for 48 hours before drawing conclusions.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.