name: "cs-linkedin-analyze"
description: "/cs:linkedin-analyze — Read your own exported LinkedIn post data, report medians and outlier bands rather than misleading means, test candidate patterns against a seeded permutation null with multiple-comparisons accounting, and size a real experiment. Refuses to conclude anything below 10 posts."
argument-hint: "[path to your LinkedIn post export, or the pattern you think you see]"
/cs:linkedin-analyze — Describe honestly, refuse to over-conclude
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
- A description — median and MAD, percentile bands, a 1.5×IQR breakout threshold, and a
per-post band from BREAKOUT to DUD.
- 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.
- 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.
Related
