---

name: ops-posthog-funnel-debugger
description: Use when analysing conversion drop-off in a defined product funnel (signup → first chat → first draft → first save → upgrade) using PostHog. Produces step-by-step conversion percentages, median time-to-step, cohort-segmented drop-off reasons, path analysis between steps, and three ranked hypotheses for the biggest leak — each paired with an actionable experiment recommendation.
license: MIT
metadata:
id: ops.posthog-funnel-debugger
category: ops
jurisdictions: [multi]
priority: P2
intent: [posthog, ops, funnel, conversion, drop-off]
related: [ops-feature-flag-experiment-launcher, ops-posthog-cohort-builder, ops-churn-risk-detector]
source: Louis — HAQQ Legal AI (github.com/sboghossian/mini-claude-for-legal)
version: "1.0"

Ops — PostHog Funnel Debugger

Purpose

A funnel analysis tells you where users are dropping off. A funnel debugger tells you why, and what to do about it. This skill wraps PostHog funnel analysis with a structured diagnostic methodology — segmenting drop-off by cohort, exploring user paths between steps, and generating concrete experiment hypotheses ranked by expected impact.

When to use this

Use this skill when:

• Conversion rate at a key funnel step has declined ≥10% week-over-week or month-over-month.
• A new feature was released and you want to understand its impact on the conversion funnel.
• You are preparing an experiment hypothesis and need data to confirm the problem exists.
• A stakeholder asks "where are users getting stuck?"

Canonical legal AI funnel

The default funnel for a legal AI product:

Step 1: Signup (account created)
Step 2: First substantive prompt sent
Step 3: First draft saved (or first document analysis completed)
Step 4: Matter created (user organizes work into a matter)
Step 5: Upgrade to paid plan

Adjust steps based on the specific product flow — this is a starting template.

Analysis steps

1. Run the funnel in PostHog

Configure the PostHog funnel with:

• Conversion window: 14 days (allow enough time for deliberate users)
• Counting method: unique users (not events)
• Date range: last 30 days (or align with the change you're investigating)
• Breakdown: by the relevant cohort dimension (persona, tier, acquisition channel)

2. Extract step-by-step conversion

For each step transition, record:

• Conversion rate (% of users who proceeded from step N to step N+1)
• Absolute number of users (don't let a high % mask a small absolute number)
• Median time from step N to step N+1

This produces a table like:

Transition	Conversion	Median time to step
Signup → First prompt	68%	2 hours
First prompt → First draft saved	41%	1 day
First draft → Matter created	52%	3 days
Matter created → Upgrade	18%	12 days

3. Segment drop-off

For the step with the worst conversion rate, compare cohorts:

• Lawyer vs consumer — does one persona drop off more?
• Acquisition channel — do users from LinkedIn convert differently than organic?
• Plan tier — do trial users behave differently than free direct signups?
• Time of signup — did a recent cohort perform worse (suggesting a product change broke something)?

4. Path analysis between steps

For users who dropped off at the worst step, use PostHog's path analysis to see what they did instead of proceeding:

• Did they navigate to the settings page? (confusion about the product)
• Did they trigger an error event? (bug preventing conversion)
• Did they come back the next day and convert? (delay, not abandonment)
• Did they exit the app immediately? (activation failure)

5. Generate three hypotheses

Based on the conversion data, segmentation, and path analysis, generate exactly three ranked hypotheses for the largest drop-off point:

Format each hypothesis as:

• Problem: What behaviour is causing the drop-off?
• Evidence: What data supports this?
• Experiment: What would we change to test this?
• Expected impact: How much could conversion improve?

Example:

Hypothesis 1 — Friction at the first draft step
Problem: Users are typing long prompts but abandoning before saving a draft.
Evidence: Median time at step 3 is 3 hours; path analysis shows 40% of dropoffs go to the error page.
Experiment: Fix the PDF upload error affecting users who try to draft from an uploaded document.
Expected impact: +8–12% conversion at step 3.

6. Recommend experiments

For the top hypothesis, link to [[ops-feature-flag-experiment-launcher]] with:

• A drafted hypothesis statement
• The primary metric (conversion rate at the drop-off step)
• Two guardrail metrics
• The cohort to test on

Output format

Deliver the funnel debug as a brief structured report:

1. Funnel table (step-by-step conversion + median time)
2. Worst-performing step identified
3. Segmentation breakdown for that step
4. Path analysis summary (top 3 paths for dropoffs)
5. Three hypotheses (ranked by expected impact)
6. Recommended experiment for the top hypothesis

Related skills

• [[ops-feature-flag-experiment-launcher]] — execute the experiment recommended by this analysis
• [[ops-posthog-cohort-builder]] — build the cohort breakdowns used in the funnel segmentation
• [[ops-churn-risk-detector]] — funnel dropoffs at the upgrade step feed the churn risk model