Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/rshankras/claude-code-apple-skills/analytics-interpretationnpx skills add rshankras/claude-code-apple-skills --skill analytics-interpretationgit clone --depth 1 https://github.com/rshankras/claude-code-apple-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rshankras/claude-code-apple-skills/analytics-interpretation)<a href="https://agentmods.dev/skills/rshankras/claude-code-apple-skills/analytics-interpretation"><img src="https://agentmods.dev/badge/skills/rshankras/claude-code-apple-skills/analytics-interpretation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.04735 |
| Opus 5 | $0.00034 | $0.02367 |
| Sonnet 5 | $0.00014 | $0.00947 |
| Haiku 4.5 | $0.00007 | $0.00473 |
Grade A, and why
analytics-interpretation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 479 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Interpretation
Interpret your app's metrics, diagnose problems, and make data-driven decisions. Works with App Store Connect data, third-party analytics, or raw numbers the user provides.
When This Skill Activates
Use this skill when the user:
- Wants to understand their app metrics or analytics
- Asks about retention, LTV, ARPU, or churn
- Wants to know if their metrics are good or bad
- Needs help interpreting App Store Connect analytics
- Wants a data-driven growth plan
- Asks "what should I focus on to grow?"
- Has metrics data and wants to know what it means
Process
Step 1: Gather Context
Ask the user via AskUserQuestion:
- App type and monetization model
- Free with ads, freemium, subscription, paid upfront, or hybrid?
- Current metrics they have access to
- App Store Connect? Third-party analytics (Mixpanel, Firebase, Amplitude)?
- Specific numbers they can share
- Downloads, DAU/MAU, retention, revenue, conversion rates?
- What they want to know
- "Are my metrics good?" / "What should I fix?" / "Should I keep going?"
Also pull App Store Connect peer group benchmarks (App Analytics → Benchmarks) before interpreting any trend — they establish whether a metric is "bad for you" or "bad for the category."
How Peer Group Benchmarks Work
- Peer group = App Store category + business model (free / freemium / paid / paidmium / subscription) + download-volume band
- Benchmarked metrics: conversion rate, D1/D7/D28 retention, crash rate, average proceeds per paying user
- You see the peer group's 25th / 50th / 75th percentile bands (example: day-1 retention 13.4% / 21.3% / 27.4%)
- Differential privacy adds noise and groups have minimum sizes — judge by which quartile you're in, not exact deltas
- Improving ≠ done: an app that lifted conversion +5.5% over 90 days can still sit in the bottom half of its peer group
| Below peers on... | Reach for... |
|---|---|
| Conversion rate | Product Page Optimization + Custom Product Pages |
| Retention | In-app events + App Clips |
| Proceeds per paying user | Pricing tier review + promoted in-app purchases |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 479 lines · 68 tokens per session scan A c4e1a4e176c3
analytics-interpretation is a skill published in the GitHub repository rshankras/claude-code-apple-skills (701 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 4,735 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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