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ProductCitation IndexAI InfluenceBlogBook
[unCited]/machine-learning in Python
ProductCitation IndexAI InfluenceBlogBook
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AEO Score

74

Strong Presence

Avg Prompt Score

13

across 469 prompts

AI Share of Voice

10%

across 446 prompts

Critical Issues

4

critical + high

Per-stage performance

🔍Discovery
442 category
Cited12%54/442
Share of voice10%avg
Engine consensus33%of engines
Competitors2.2avg/cited
Sentiment—no data
⚖️Evaluation
15 brand-level
Cited73%11/15
Share of voice73%avg
Engine consensus—
Competitors0.5avg/cited
Sentiment—no data
🛡️Trust
5 brand-level
Cited60%3/5
Share of voice60%avg
Engine consensus—
Competitors0.0avg/cited
Sentiment—no data
💰Conversion
6 brand-level
Cited67%4/6
Share of voice67%avg
Engine consensus—
Competitors0.3avg/cited
Sentiment—no data

Cited rate · share of voice · engine consensus · sentiment, broken out by buyer-journey stage. Sentiment is the net positive−negative skew across engines that cited the brand at this stage.

Categories machine-learning in Python is visible in

3
  • AI Codingnot yet measured→
  • Developer Toolsnot yet measured→
  • LLM Platformsnot yet measured→

Executive summary

GitHub is highly likely to be cited by AI engines for evaluation-stage prompts like "What is the best version control hosting software?" and "How does GitHub compare to GitLab?" because it has strong third-party review authority (G2 + Gartner Peer Insights) and first-party comparison content on GitHub Resources. The single highest-ROI fix is to improve crawlable, structured pricing + evaluation Q&A (FAQPage/SoftwareApplication) so prompts like "How much does GitHub cost?" and "Is GitHub worth it for a mid-market team?" can be answered from GitHub-owned pages rather than only from third parties.

Based on audit of github.com · Apr 2, 2026

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