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/cs-linkedin-analyze

Slash Command Source

Command: /cs:linkedin-analyze [export path or the claim to test]

Your own data only. Export from LinkedIn Analytics → Post impressions → Export, or Settings → Data privacy → Get a copy of your data. Nothing is fetched; scraping post data is prohibited by User Agreement §8.2 and none of this needs it.

When to run

  • "Why did my reach drop?"
  • "Do carousels actually do better for me?"
  • "What's working?"
  • Before changing strategy on the basis of one post that did well

What you get

  1. A description — median and MAD, percentile bands, a 1.5×IQR breakout threshold, and a per-post band from BREAKOUT to DUD.
  2. A verdict on the pattern — SUPPORTED, NOT_SUPPORTED, TOO_SMALL, or NOT_TESTED, with the reason for each, plus how many candidates would pass on noise alone.
  3. A sized experiment if something survived — or an honest "this needs more posts than a quarter allows".

Workflow

python3 ../skills/linkedin-analytics/scripts/post_performance_analyzer.py \
  --input export.csv --csv --output human
#   exit 2 = under 10 posts. Descriptive only. Say so and stop.

python3 ../skills/linkedin-analytics/scripts/pattern_miner.py \
  --input export.csv --csv --output human
#   exit 2 = nothing survived. This is a real finding, not a failure.

# CV for the planner = 1.4826 * MAD / median, from step one
python3 ../skills/linkedin-analytics/scripts/experiment_planner.py \
  --hypothesis "..." --variable "..." --cv 0.45 --effect 0.30 \
  --posts-per-week 2 --max-weeks 12 --output human

Discipline

  • Under 10 posts, describe; do not conclude. State it plainly rather than hedging into something that reads like a conclusion.
  • "Nothing survived" is the most common honest answer. Report it as a finding.
  • A pattern in past posts is a hypothesis. Retrospective data is confounded — you made carousels when you had structured material, on topics you knew best, in weeks you had time.
  • Never benchmark against someone else's numbers. Different denominator, different audience, usually a vendor's sample.
  • Follower count is not a success metric. Point them at the Tier 1 log instead.
  • One good post is not evidence. It is the least informative event available.

Stop conditions

  • Description delivered and the user knows which three outcome metrics to log by hand → done.
  • Miner returns nothing supported → say so, recommend re-running in six weeks, and stop. Do not keep slicing the data until something passes.
  • Experiment planner says TOO_LONG → present the minimum detectable effect in their window and let them decide. Do not quietly shrink the effect to make it fit.