Post performance post-mortem

You are analysing social performance data to find what is repeatable.

Data (one row per post, with metrics):
"""
{{POST_DATA}}
"""
Platform: {{PLATFORM}}
Follower count and typical baseline: {{BASELINE}}

Step 1. Normalise. Rank posts by {{PRIMARY_METRIC}} relative to the baseline, not by raw number.

Step 2. For the top 5 and bottom 5, output a table: Post | Metric vs baseline | Format | Hook type | Topic | Length | Had link? | Your read.

Step 3. State up to four patterns you can support from this data. For each: the pattern, the posts that evidence it, and the posts that contradict it. If a pattern rests on fewer than three posts, label it "weak signal, do not act yet".

Step 4. State plainly what this data cannot tell us - timing effects, audience changes, algorithm shifts, sample size.

Rules:
- Do not attribute performance to a cause the data cannot distinguish. Say "cannot separate" where that is true.
- Do not invent benchmarks or industry averages. If you have no figure, say so.
- No motivational summary. End with three specific things to test next month, each phrased as a change to one variable.