App analytics

Skill asodevapp/skills/skills/app-analytics

Use when the user wants to interpret ASO.dev Measurements or App Store Connect metrics: downloads, impressions, product page views, conversion, revenue, retention, sessions, active devices, ratings, reviews, release markers, experiment impact, or before/after ASO performance.From its SKILL.md

Install

npx -y skills add asodevapp/skills --skill app-analytics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.6 KB, as published. Nobody here has run it

App Analytics

Interpret ASO and App Store performance data and turn it into decisions for metadata, creatives, pricing, reviews, releases, and experiments.

ASO.dev Measurements context

ASO.dev Measurements compares current vs previous periods and shows release-date markers on charts. Use those release markers to connect metric changes to metadata, screenshot, pricing, event, or version changes.

Common metrics:

impressions, product page views, downloads, conversion
in-app purchases, subscription/revenue metrics when available
retention, sessions, active devices
ratings, reviews, review velocity
release dates and before/after periods

Workflow

Identify the question: conversion drop, install growth, revenue change, retention, experiment result, review/rating movement, or release impact.
Capture period, country, locale, source/channel, app version, and changed surfaces.
Compare current period to previous period and mark releases/campaigns.
Separate correlation from causation.
Recommend the smallest next experiment or investigation.

Launch or major update readout

For a new launch or major update, inspect the first 14 days closely when data exists:

compare daily trend against release markers and previous comparable period
watch conversion, downloads, ratings/reviews, crashes/complaint themes if supplied, and revenue/subscription starts if relevant
distinguish launch novelty, featuring/ad bursts, seasonality, and metadata/creative effects
turn findings into the next metadata, screenshot, review, pricing, or CPP/PPO action

Output

# App Analytics Readout

App:
Market:
Period:
Comparison period:
Question:

## Metric changes
| Metric | Current | Previous | Change | Interpretation |
|---|---:|---:|---:|---|

## Timeline
| Date | Release/campaign/change | Metrics affected | Notes |
|---|---|---|---|

## Diagnosis
- Likely driver:
- Alternative explanations:
- Data gaps:

## Next actions
| Priority | Action | Skill/tool | Success metric |
|---|---|---|---|

Rules

Do not invent metrics or attribution.
Do not call a change causal without enough evidence.
Segment by country, locale, traffic source, and version when possible.
For subscription-specific churn, trial, billing, or win-back analysis, use subscription-lifecycle.