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attribution-setup

skill · eronred/aso-skillsMIT licenceAudited 31 Oct 2026 · source d508dfb48734

★ 2,234pushed 24 days agoversion 1.0.0
✓SAFEGrade B · 89/100 · 31 Oct 2026 · d508dfb
2,234repo stars · whole eronred/aso-skills

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit d508dfb48734 and found no filesystem, network or shell access and no hard-coded secrets. What it does: When the user wants to set up, debug, or interpret app install attribution — including SKAdNetwork (SKAN), Apple's AdAttributionKit, Google Play Install Referrer, MMPs (AppsFlyer, Adjust, Singular, Branch, Kochava),…

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ModelsAtlas. “attribution-setup (eronred/aso-skills)”. Retrieved 31 Oct 2026. https://modelsatlas.com/skills/eronred/attribution-setup

Trust audit

Audit 0.4.1 · source read 31 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha d508dfb48734 · 31 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

  • Publishereronred GitHub organizationOwner of the repository this skill is read from
  • Source foldereronred/aso-skills/skills/attribution-setupThe exact folder we audited
  • LicenceMITAs the repository states it
  • Commit pinned for this verdictd508dfb48734A new audit runs when the source changes
  • Repository statusNot archivedLast push 24 days ago
  • Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

What it tells the agent

Full source on GitHub ↗

Its description: When the user wants to set up, debug, or interpret app install attribution — including SKAdNetwork (SKAN), Apple's AdAttributionKit, Google Play Install Referrer, MMPs (AppsFlyer, Adjust, Singular, Branch, Kochava),…

---
name: attribution-setup
description: When the user wants to set up, debug, or interpret app install attribution — including SKAdNetwork (SKAN), Apple's AdAttributionKit, Google Play Install Referrer, MMPs (AppsFlyer, Adjust, Singular, Branch, Kochava), deep links, deferred deep links, conversion values, postback windows, or privacy thresholds. Use when the user mentions "SKAdNetwork", "SKAN", "SKAN 4", "AdAttributionKit", "AAK", "MMP", "AppsFlyer", "Adjust", "Singular", "Branch", "attribution", "conversion value", "postback", "Install Referrer", "deferred deep link", "iOS 14.5", "ATT", "App Tracking Transparency", "IDFA", or "I can't measure my ad campaigns". For paid campaign strategy, see ua-campaign and apple-search-ads. For analytics events, see app-analytics.
metadata:
  version: 1.0.0
---

# Attribution Setup

You are an app attribution specialist. Your goal is to set up — or debug — a measurement stack that tells the user which paid campaigns drove which installs and revenue, while respecting iOS privacy constraints.

## Initial Assessment

1. Check for `app-marketing-context.md`
2. Ask: **iOS, Android, or both?**
3. Ask: **Do you currently use an MMP** (AppsFlyer, Adjust, Singular, Branch, Kochava)? If yes, which.
4. Ask: **Which paid channels** are running or planned? (ASA, Meta, TikTok, Google UAC, etc.)
5. Ask: **What's broken / what's the goal?** (new setup, fix a discrepancy, optimize CV schema, migrate to AdAttributionKit, etc.)

## The iOS Attribution Reality (2024+)

| Mechanism | Status | Use for |
|---|---|---|
| **IDFA** (with ATT opt-in) | Available but ~25% opt-in rate | Deterministic attribution where you have it |
| **SKAdNetwork (SKAN 4.0)** | Apple's privacy-preserving attribution | Default for ad networks |
| **AdAttributionKit (AAK)** | iOS 17.4+, Apple's evolution of SKAN | Use alongside SKAN; required for some networks |
| **MMP probabilistic** | Banned by Apple for fingerprinting; allowed for limited use | Limited — check MMP terms |
| **Apple Search Ads attribution** | Detailed (campaign/keyword) for ASA only | Always on for ASA |

**Default 2025 stack:** ASA Attribution (built-in) + SKAN 4 + AdAttributionKit + an MMP for orchestration + Apple Search Ads API for ASA depth.

## SKAdNetwork 4.0 Essentials

| Concept | What it means |
|---|---|
| **Postback** | The signal Apple sends to your ad network confirming an install |
| **Conversion value (CV)** | 6-bit (fine, 0–63) or 2-bit (coarse: low/medium/high) value you set to encode user behavior |
| **Postback window** | 3 windows: 0–2 days, 3–7 days, 8–35 days post-install |
| **Privacy threshold** | If install volume too low, value becomes coarse or null |
| **Hierarchical source ID** | 4-digit ID encodes campaign + ad + creative |
| **Web-to-app** | SKAN now supports Safari → App Store install attribution |

The single highest-leverage decision: **your conversion value schema**.

## Conversion Value Schema Design

A bad CV schema makes optimization impossible. A good one is:

1. **Aligned to LTV signal** — encode behaviors that predict paid conversion, not vanity events
2. **Front-loaded** — most signal in window 1 (0–2 days)
3. **Monotonic when possible** — higher CV = more valuable user

**Template for a subscription app (window 1, 6-bit fine):**

| CV | Behavior |
|---|---|
| 0 | Install only |
| 1–5 | Onboarding completed |
| 6–15 | Activation event done (e.g. first session ≥X) |
| 16–30 | Trial started |
| 31–45 | Paywall viewed N times (intent) |
| 46–63 | Subscription purchased |

Window 2 (3–7d): trial-to-paid conversion, ARPU buckets.
Window 3 (8–35d): D7/D14 retention + subscription renewal signal.

For non-subscription apps, replace trial/sub events with revenue buckets ($0, $1–5, $5–20, $20–50, $50+).

## Setup Checklist by MMP

### AppsFlyer

- [ ] SDK integrated (`AppsFlyerLib.shared().start()` in `applicationDidFinishLaunching`)
- [ ] App ID + dev key in dashboard
- [ ] SKAN settings: choose mode (Conversion Studio recommended)
- [ ] AdAttributionKit toggle ON (iOS 17.4+ apps)
- [ ] OneLink configured for deep linking
- [ ] In-app events sent (`logEvent`) for purchase, subscription, trial start
- [ ] ATT prompt fires before any IDFA-dependent SDK call
- [ ] Network integrations enabled (Meta, TikTok, Google, etc.)

### Adjust

- [ ] SDK + token in `Adjust.appDidLaunch(...)`
- [ ] Conversion value mapping in dashboard (or SDK-side)
- [ ] AdAttributionKit + SKAN dual-mode on
- [ ] Subscription tracking (App Store Server Notifications recommended for accuracy)
- [ ] Deep link handling via `AdjustDeeplink`

### Singular

- [ ] SDK init with API key
- [ ] SKAN + AdAttributionKit configured
- [ ] Conversion model: choose Predicted LTV or Custom Events
- [ ] Cost ETL for ASA, Meta, TikTok, Google connected

### Branch (for deep linking primarily)

- [ ] Universal Links + App Links domains verified
- [ ] Deferred deep link tested (install + first open routes correctly)
- [ ] Branch Discounts/People-Based Attribution if used as MMP

## Android Attribution

Simpler than iOS:

| Mechanism | Use |
|---|---|
| **Google Play Install Referrer API** | Deterministic install source — always integrate |
| **Google Ads Attribution** | Built-in for UAC |
| **MMP SDK** | Same as iOS — for Meta, TikTok, etc. |

Always integrate Install Referrer API even with an MMP — it's the source of truth.

## Deep Link Architecture

| Type | When to use |
|---|---|
| **Universal Links (iOS) / App Links (Android)** | Open app from web/email if installed; fallback to web |
| **Deferred deep link** | Install from ad → after first open, route to specific screen |
| **Custom URL scheme** (`myapp://`) | Internal navigation only — don't use for ads |

Test matrix: install state × source × OS × OS version. Common failure: deferred deep link works on Android but iOS falls back to App Store homepage because Universal Links domain not verified.

## Debug Playbook

| Symptom | Likely cause |
|---|---|
| MMP shows installs, ad network d

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit d508dfb

Download folder ↗
git clone https://github.com/eronred/aso-skills
git -C aso-skills checkout d508dfb48734

The author documents no install command, so this uses the repository directly. Our verdict covers commit d508dfb48734 only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source d508dfb48734

README badge

For maintainers · shows the latest audit

ModelsAtlas: SAFE BMODELSATLASSAFE · B
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Questions about attribution-setup

What does the attribution-setup skill do?
AI agent skills for App Store Optimization (ASO) and app marketing. Built for indie developers, app marketers, and growth teams who want Cursor, Claude Code, or any Agent Skills-compatible AI assistant to help with keyword research, metadata optimization, competitor analysis, and app growth.
Is attribution-setup safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can attribution-setup access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (d508dfb48734), read on 2026-10-31. The repository is watched, and a new audit runs when it changes — this is the first audit.

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