antigravity-ipa-workflow/skills/lean-analyze-usage/SKILL.md

name: lean-analyze-usage description: Analyze post-launch usage data to generate improvement recommendations with prioritized action items. Use after a product has launched and usage data is available, when identifying feature adoption gaps or funnel drop-offs, or when planning the next iteration based on real user behavior. Trigger phrases: "analyze usage data", "post-launch analysis", "lean usage analysis".

Overview

Generates a usage analysis report from analytics data (CSV or manual input) to inform the next product iteration. Outputs plans/reports/usage-analysis-{date}.md with findings, funnel analysis, retention data, and prioritized recommendations.

When to Use

• Product has launched and 30+ days of usage data are available
• Identifying which features are underused or causing drop-off
• Planning the next improvement cycle based on real behavior
• Presenting data-driven recommendations to stakeholders

Don't Use When

• Product has not launched yet (use lean-user-research instead)
• No usage data is available

Input

Supported:

• CSV file path (exported from GA, Mixpanel, Amplitude, etc.)
• "manual" (guided input via questions)

Workflow

Step 1: Collect Data

If CSV provided: parse user_id, event, timestamp, properties.

If "manual": ask for:

• Total users (DAU/WAU/MAU)
• Key events (signups, activations, feature usage)
• Funnels (conversion rates)
• Retention (D1, D7, D30)

Step 2: Analyze Patterns

• Feature usage: most/least used features, power vs casual users
• Drop-off analysis: where users abandon flows, conversion per step
• Retention: return rates after 1/7/30 days, cohort quality
• Correlations: features correlated with retention, acquisition source vs conversion

Step 3: Generate Report

# Usage Analysis Report

**Period:** {start} to {end}
**Generated:** {date}

## Executive Summary
**Key Findings:**
1. Finding 1 (Impact: HIGH)
2. Finding 2 (Impact: MEDIUM)
**Top Opportunity:** [Highest impact improvement]

## Metrics Overview
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| MAU | X | 1,000+ | pass/warn |
| Signup → Activation | X% | 40%+ | pass/warn |
| D7 Retention | X% | 20%+ | pass/warn |

## Feature Adoption
| Feature | Users | % of Total | Trend |
|---------|-------|------------|-------|

## Funnel Analysis
| Step | Users | Conversion | Drop-off |
|------|-------|------------|----------|

## Cohort Retention
| Source | Users | D1 | D7 | D30 | Quality |

## Recommendations

### P0 (Do First)
**1. [Issue Title]**
- Issue: [Problem]
- Root Cause: [Why]
- Solution: [Fix]
- Expected Impact: [Metric improvement]

### P1 (Medium Priority)
### P2 (Low Priority)

## Next Steps
1. Review with team
2. Create improvement plan: lean [improvement]
3. Implement P0 recommendations
4. Re-analyze in 30 days

## Data Sources
- Platform: {name}
- Date Range: {range}
- Limitations: {data quality notes}

Integration

Launch MVP
    ↓
Collect usage data (30+ days)
    ↓
lean-analyze-usage → plans/reports/usage-analysis-{date}.md
    ↓
lean [improvement] → Next iteration
    ↓
plan → Implement improvements