Agent skill · alirezarezvani
linkedin-analytics
Use when someone wants to understand their own LinkedIn numbers — which posts worked, why reach dropped, whether a pattern is real, or how to test a hypothesis. Triggers on "why did my reach drop", "what's working on my LinkedIn", "analyze my posts", "do carousels do better for me", "should I test this", "LinkedIn analytics". Reads your own exported post data, reports medians and outlier bands, tests candidate patterns against a permutation null, and sizes a real experiment — refusing to conclude anything below 10 posts.
What it needs
About 3k tokens when loaded.
What this skill does
LinkedIn Analytics — describe honestly, then refuse to over-conclude The characteristic sentence of LinkedIn analytics is "carousels do 3x better for me", built on four posts. With engagement as heavy-tailed as it is, four posts will show a 3x difference between almost any two groups you care to define. These three scripts stop that sentence becoming a strategy. Your own data only. Nothing is fetched; scraping post or profile data is prohibited by User Agreement §8.2 and none of this analysis needs it. Workflow 1. Get the export. LinkedIn Analytics → Post impressions → Export, or Settings → Data privacy → Get a copy of your data. CSV and JSON both work. 2. Describe it. Exit 0 analysed / 2 below the 10-post floor, descriptive only / 3 unusable. Reports median and MAD rather than mean and standard deviation — one breakout post makes a mean describe a distribution none of your posts belong to — plus Tukey percentile bands and a 1.5×IQR breakout threshold, so "this did well" has a number behind it. 3. Test the pattern they think they see. Exit 0 something survived / 2 nothing survived / 3 under 10 posts. Four gates: 5 posts in and 5 out; a 15% relative difference in medians; beating 90% of 2,000 seeded label shuffles; and a multiple-comparisons accounting of how many candidates would pass on noise alone. "Nothing survived" is the most common honest answer and it is a real finding. Report it as one. Do not soften it into a hedge that reads like a conclusion. 4. Turn a survivor into a test. CV comes from step 2: 1.4826 MAD / median. Exit 0 feasible / 2 too long, with the minimum detectable effect in their window / 3 refused. It will frequently say the test needs more posts than a quarter allows — that is the honest answer, and more useful than a confident conclusion from retrospective data. Rules Under 10 posts, describe; do not conclude. Say so plainly. A pattern in past posts is a hypothesis. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/linkedin-analytics--bb1254