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configuring-experiment-analytics

Skill

Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handl

0Analytics

Install

git clone https://github.com/PostHog/ai-plugin.git ~/.claude/skills/configuring-experiment-analytics

What is configuring-experiment-analytics?

Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~237 tokens of context used while enabled, before you invoke anything

Documentation

README · ~8 min read

Configuring experiment analytics

This skill answers: Who is included in the analysis? and How to measure impact?

Exposure criteria

Exposure criteria determine which users are counted in the experiment analysis.

Include people when

Two options:

  1. Feature flag called (default) — users are included when the $feature_flag_called event fires for the experiment's flag. This is the standard approach — it means a user is included only when they actually encounter the feature flag in your code.
  2. Custom exposure event — users are included when a specific custom event fires. Use this when you want tighter control over who enters the analysis (e.g., only users who actually visit the page where the experiment runs).

Multiple variant handling

When a user is exposed to multiple variants (e.g., due to flag changes or race conditions):

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