/cs-linkedin-analyze

disable-model-invocation: true
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

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 ${CLAUDE_PLUGIN_ROOT}/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 ${CLAUDE_PLUGIN_ROOT}/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 ${CLAUDE_PLUGIN_ROOT}/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

• Skill: [`linkedin-analytics`](../skills/linkedin-analytics/SKILL.md)
• Log: [`measurement_log_template.md`](../skills/linkedin-analytics/assets/measurement_log_template.md)
• Reference: [`evidence_thresholds.md`](../skills/linkedin-analytics/references/evidence_thresholds.md)