Reel scorecard: the 72-hour read

Last step of the pipeline: idea -> script -> record -> "the card is in" -> edit -> trial-post -> reel-scorecard, and it feeds the next reel-ideation run (winning hooks and formats get copied again, losers retire).

Requirements

Claude Code (or Claude.ai with a CSV export pasted). The Zernio MCP connector for read-only analytics, and the trial-post ledger ($HOME/.trial-post/ledger.jsonl), which carries each post's idea and hook ids. cp config.example.json config.json to change the thresholds.

Rules that keep the numbers honest

• Same source, same window. Only Zernio numbers against Zernio numbers, only 72 to 96 h old against each other. Instagram insights lag up to 48 h, TikTok fills in 24 to 48 h: a 24 h look is a peek, never a decision.
• Views are shown, never used alone. Instagram: skip rate and hold (average watch / length) decide. TikTok: completion rate first, For You share next to it.
• No peak without its decay. Quote a number with its window and date, or do not quote it.
• Follows are not in the API for reels. The creator checks follows for the finalists in the app before graduating.

Steps

1. Pull (read-only). For every ledger post 72+ h old: mcp__zernio__analytics_get_analytics with post_id (or with platform + from_date for a batch, paginated). Save each result to scorecard/raw/<date>-<n>.txt.
2. Score. python3 scripts/scorecard.py --ledger $HOME/.trial-post/ledger.jsonl --analytics scorecard/raw/*.txt --out scorecard/<date>.md
3. Read it out to the creator, short: per idea, the winning hook and why (skip, hold), graduate candidates (max 2 a day so the main feed is not flooded), kills, retirements, and what is not in the window yet.
4. Graduation is the creator's tap: Instagram app > the trial reel > "Share to everyone". There is no API for it. One graduation per idea. Never re-upload the trial file through another scheduler (duplicate content); a main-feed repost of a winner is a new cut or a new take.
5. Weekly learning log (once a week): for every idea with 2+ hooks scored, which hook TYPE won (callout, belief flip, proof...), which format, which opening visual. Append to scorecard/LEARNING-LOG.md. Change at most ONE rule per week, and only after 3+ tests agree. Feed the winners to the next reel-ideation run.

Default thresholds (config.example.json; tune with your own numbers after two weeks)

decision	Instagram rule
graduate candidate	lowest skip AND hold >= 28% AND skip < 55% AND >= 1,000 views
split	best skip and best hold on different hooks: the creator picks
tie	skip within 2 points (skip rate is an estimate)
kill	skip >= 65%: that hook never graduates and is not reused on that idea
retire the idea	every hook >= 60% skip and under ~1,300 views

These come from one creator's account; they are a starting point, not a law.

Output

scorecard/
  raw/<date>-<n>.txt     saved analytics results (never edited)
  <date>.md              the scorecard
  LEARNING-LOG.md        one dated block per week, the one rule changed (if any) and the tests behind it

Failure handling

• A post missing from analytics: it may still be processing; list it under "not in window yet".
• Ledger has no idea/hook ids (old posts): add them by hand to the ledger lines, or score those posts by eye.
• Numbers look impossible (skip 0, views 0 after days): say so, do not decide on them, check the post in the app.