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/markdavidgan/apple-dev-skills/app-analyticsnpx skills add markdavidgan/apple-dev-skills --skill app-analyticsgit clone --depth 1 https://github.com/markdavidgan/apple-dev-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/markdavidgan/apple-dev-skills/app-analytics)<a href="https://agentmods.dev/skills/markdavidgan/apple-dev-skills/app-analytics"><img src="https://agentmods.dev/badge/skills/markdavidgan/apple-dev-skills/app-analytics.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 | $0.00111 | $0.01083 |
| Opus 5 | $0.00056 | $0.00541 |
| Sonnet 5 | $0.00022 | $0.00217 |
| Haiku 4.5 | $0.00011 | $0.00108 |
Grade A, and why
app-analytics 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 4d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Analytics
Measure the few things that drive decisions — not everything you can. Over-instrumenting creates noise, privacy risk, and a dashboard nobody reads. This skill is about what to measure; performance telemetry is performance-instruments (MetricKit).
Test for every metric: if this number moved, would we do something differently? If not, don't track it.
Start with one north-star metric
A north-star is the single number that best captures delivered user value — leading, not lagging.
- Good: "weekly active note editors", "sessions completed per user/week", "tracked workouts/week."
- Weak: raw downloads or DAU alone (vanity; doesn't capture value).
- The north-star should predict retention and revenue. Everything else is a supporting or guardrail metric.
The funnel (AARRR, pragmatically)
| Stage | Question | Typical metric |
|---|---|---|
| Acquisition | Are people arriving? | Impressions → installs (see asc-aso) |
| Activation | Did they reach first value? | % completing the "aha" action in session 1 |
| Retention | Do they come back? | D1 / D7 / D30 cohort retention |
| Revenue | Do they pay? | Trial→paid, conversion, ARPU (see paywall-design, app-store-pricing) |
| Referral | Do they bring others? | Invites sent/accepted, rating prompts |
Activation and retention are where most apps actually win or lose. Define your activation event precisely ("created and saved 1 note", "logged 3 days") and measure the % who hit it on day 0 — it's the highest-leverage number for a young app.
Retention is a cohort, not a percentage
Always read retention as cohorts (users who installed in week N, % active in weeks N+1, N+7…). A single "retention %" hides whether you're improving. A flattening retention curve (not decaying to zero) is the signal of product-market fit.
Event taxonomy — design it before you instrument
A messy event log is worse than none. Conventions:
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.
- 4d ago First seen · 80 lines · 111 tokens per session scan A 5480c1f3d626
app-analytics is a skill published in the GitHub repository markdavidgan/apple-dev-skills (5 stars, last pushed 5d ago), licensed MIT. It adds 111 tokens to every session and 1,083 once invoked, about $0.0006 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 2026-08-31.
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