Post Performance Debrief

Stop guessing why a post worked—or did not

Post Performance Debrief turns the content and metrics you already have into a structured learning loop. It separates what the data actually shows from what is merely plausible, calculates only defensible rates, and gives you three controlled next-post tests.

What you get

• A data-quality audit covering metric labels, reporting windows, denominators, and missing evidence.
• Clearly separated Observed, Derived, Hypothesis, and Unknown findings.
• Safe rate calculations with formulas and named denominators.
• A content-mechanics review of hook, structure, proof, pacing, CTA, and format fit.
• A performance fingerprint across attention, retention, resonance, distribution, action, and evidence coverage.
• Ranked explanations with evidence for and against each one.
• Exactly three controlled experiments that change one primary variable at a time.
• A concise Preserve / Change / Stop summary.

Built for creators who want useful learning, not algorithm folklore

The skill never invents metrics, universal benchmarks, audience reactions, or platform causes. Missing evidence stays missing. A single post produces provisional hypotheses—not fake certainty or growth guarantees.

Best inputs

The published caption, script, transcript, or post text; platform and format; at least one observed metric; and optional goal, reporting window, comparable baseline, and context.

Known limitations

This skill does not connect to social accounts, scrape private dashboards, fetch live analytics, create posts from scratch, analyze paid campaigns, guarantee reach, or prove algorithmic causality. Cross-platform metrics are analyzed separately unless their definitions and conditions are genuinely comparable.