diff --git a/marketing/app-store-optimization/SKILL.md b/marketing/app-store-optimization/SKILL.md
index 618dde81..c9a6cfa1 100644
--- a/marketing/app-store-optimization/SKILL.md
+++ b/marketing/app-store-optimization/SKILL.md
@@ -3,6 +3,8 @@ name: app-store-optimization
 description: >
   App Store Optimization toolkit for researching keywords, optimizing metadata,
   and tracking mobile app performance on Apple App Store and Google Play Store.
+  Use when researching app store keywords, optimizing store metadata, auditing
+  competitor listings, planning launch experiments, or analyzing reviews.
 license: MIT + Commons Clause
 metadata:
   version: 1.1.0
@@ -71,8 +73,14 @@ Load the reference that matches the task — keep this file lean and pull detail
 
 **Out of scope:** real-time store data fetching (scripts analyze static data you provide), Apple Search Ads / Google Ads campaign management, creative asset design, cross-device attribution (use an MMP), in-app analytics/retention, and revenue/subscription pricing.
 
-**Data constraints:** no official search-volume API exists for either store (estimates use third-party tools or heuristics); competitor and review data are limited to public info; historical ranking data needs external tools (AppTweak, Sensor Tower, data.ai); Apple's June 2025 update indexes screenshot text, which these scripts do not yet analyze. See [references/operations-and-benchmarks.md](references/operations-and-benchmarks.md) for details.
+**Data constraints:** no official search-volume API exists for either store (estimates use third-party tools or heuristics); competitor and review data are limited to public info; historical ranking data needs external tools (AppTweak, Sensor Tower, data.ai, or [AppNiche](https://getappniche.com/docs/api-and-mcp)); Apple's June 2025 update indexes screenshot text, which these scripts do not yet analyze. See [references/operations-and-benchmarks.md](references/operations-and-benchmarks.md) for details.
+
+## Anti-Patterns
+
+- Do not invent search volume, ranking history, revenue, or review data when no source is provided.
+- Do not optimize metadata with keyword stuffing or competitor trademarks.
+- Do not treat paid acquisition, attribution, pricing, or retention problems as ASO-only problems.
 
 ## Integration Points
 
-Connects to **Apple App Store Connect** and **Google Play Console** (metadata submission, Product Page Optimization / Store Listing Experiments), **Apple Search Ads** (keyword discovery), **ASO tools** (AppTweak, Sensor Tower, data.ai for volume/ranking data), **analytics** (Firebase/Mixpanel/Amplitude for engagement signals), and the **campaign-analytics** and **content-creator** skills. Full connection details and data flows: [references/operations-and-benchmarks.md](references/operations-and-benchmarks.md).
+Connects to **Apple App Store Connect** and **Google Play Console** (metadata submission, Product Page Optimization / Store Listing Experiments), **Apple Search Ads** (keyword discovery), **ASO tools** (AppTweak, Sensor Tower, data.ai, AppNiche for volume/ranking/app intelligence data), **analytics** (Firebase/Mixpanel/Amplitude for engagement signals), and the **campaign-analytics** and **content-creator** skills. Full connection details and data flows: [references/operations-and-benchmarks.md](references/operations-and-benchmarks.md).
diff --git a/marketing/app-store-optimization/references/operations-and-benchmarks.md b/marketing/app-store-optimization/references/operations-and-benchmarks.md
index 8bf604f3..8b1368ed 100644
--- a/marketing/app-store-optimization/references/operations-and-benchmarks.md
+++ b/marketing/app-store-optimization/references/operations-and-benchmarks.md
@@ -109,6 +109,7 @@ All output passes quality verification:
 | **Google Play Console** | Metadata submission, Store Listing Experiments, performance reports | Apply metadata recommendations in Play Console. Use Store Listing Experiments for A/B tests. Export conversion data for `aso_scorer.py` input |
 | **Apple Search Ads** | Paid keyword discovery, Search Match insights | Use keyword data from `keyword_analyzer.py` to build Search Ads campaigns. Import Search Ads search term reports back into keyword research workflow. In 2026, leverage new inline ad placements and Maximize Conversions bidding |
 | **ASO Tools (AppTweak, Sensor Tower, data.ai)** | Search volume data, ranking tracking, competitor intelligence | Export keyword volume and competitor data from ASO tools as input for `keyword_analyzer.py` and `competitor_analyzer.py`. Feed ranking history into `aso_scorer.py` |
+| **[AppNiche API/MCP](https://getappniche.com/docs/api-and-mcp)** | iOS app revenue/download estimates, keyword metrics, rankings, reviews, and competitor signals | Pull AppNiche API or MCP outputs into `keyword_analyzer.py`, `competitor_analyzer.py`, `review_analyzer.py`, and `aso_scorer.py` when validating an iOS niche or comparing tracked competitors |
 | **Firebase / Mixpanel / Amplitude** | Post-install analytics, retention metrics | Use retention and engagement data to inform ASO scoring (engagement signals affect store rankings). Feed conversion funnel data into `aso_scorer.py` conversion metrics |
 | **campaign-analytics skill** | Attribution modeling for app install campaigns | Combine ASO organic data with paid campaign attribution from `attribution_analyzer.py` to understand full acquisition picture |
 | **content-creator skill** | App description copywriting and SEO optimization | Use `seo_optimizer.py` principles for app description writing. Apply brand voice consistency from `brand_voice_analyzer.py` across store listings |