LinkedIn Post Autopsy

Every post is data. This skill reads the post against the moves that drive distribution, reads the engagement ratios, and tells you the one thing to change next time.

Inputs

• The post text and its stats (impressions, comments, reactions, saves, profile views): $ARGUMENTS
• The brief (persona, pillars, offer, pains) loads automatically.

Do this

1. Score the post against the seven moves, one line each, present or missing:
   • Value-lever hook (first line earns the second)
   • Status reframe (shifts how the reader sees themselves or the problem)
   • System, not tips (a repeatable mechanism, not a listicle)
   • Quantified before/after (real numbers, attributed to a post / a client / an operator)
   • Contrarian line (one claim the feed disagrees with)
   • Comment-keyword CTA (asks for a specific word, not "thoughts?")
   • Saveable asset (something worth keeping: a checklist, a teardown, a swipe)
2. Read the ratios. Comments are the heaviest-weighted signal. An inverted ratio (more comments than reactions) is the viral fingerprint. Note reactions-to-comments, and saves and profile views as intent signals.
3. Tie the ratio read back to the moves. Which present move drove the signal, which missing move capped it.
4. Name the single weakest move: the one that, fixed, would have moved this post most.

Output

Return labelled: a move-by-move scorecard (seven rows, present/missing + one-line read), the ratio read (what the comments-to-reactions ratio says about distribution), and THE ONE CHANGE that would have moved it most. Hand the rewrite to /linkedin-humanizer if you redraft the hook.

Rules

• End on one fix, not a list of five. If you can't reduce to one, you haven't finished reading the stats.
• Ground every claim in the numbers given. No verdict the stats don't support.
• Attribute any quoted number to a post / a client / an operator, never a named person or company.
• No em-dashes.