REPOGEO REPORT · LITE

yzhao062/pyod

Default branch master · commit 18362cd6 · scanned 5/21/2026, 3:12:32 PM

GitHub: 9,851 stars · 1,475 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

84 /100

Healthy

Category recall

2 / 2

Avg rank #1.5 when recommended

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface yzhao062/pyod, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Strengthen README's opening line to highlight unique features

Why:

CURRENT

Python Outlier Detection (PyOD) 3

COPY-PASTE FIX

PyOD 3: The leading Python library for **agentic, multimodal anomaly detection** across tabular, time series, graph, text, and image data. It offers 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

mediumcomparison#2

Add a 'Comparison to Alternatives' section in README

Why:

COPY-PASTE FIX

## Comparison to Alternatives

Unlike general-purpose machine learning libraries such as scikit-learn, TensorFlow, or PyTorch, PyOD is exclusively focused on comprehensive anomaly detection. We offer a significantly broader range of specialized algorithms (60+), benchmark-backed orchestration, and unique agentic capabilities tailored specifically for outlier detection across diverse data types.

lowtopics#3

Add more specific topics to reinforce framework and orchestration

Why:

CURRENT

agentic-ai, anomaly-detection, data-mining, data-science, deep-learning, foundation-models, fraud-detection, graph-anomaly-detection, image-anomaly-detection, machine-learning, multimodal, nlp-anomaly-detection, novelty-detection, out-of-distribution-detection, outlier-detection, outlier-ensembles, time-series, time-series-anomaly-detection, unsupervised-learning

COPY-PASTE FIX

agentic-ai, anomaly-detection, anomaly-detection-framework, data-mining, data-orchestration, data-science, deep-learning, foundation-models, fraud-detection, graph-anomaly-detection, image-anomaly-detection, machine-learning, multimodal, nlp-anomaly-detection, novelty-detection, out-of-distribution-detection, outlier-detection, outlier-ensembles, time-series, time-series-anomaly-detection, unsupervised-learning

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

2 / 2

100% of queries surface yzhao062/pyod

Avg rank

#1.5

Lower is better. #1 = top recommendation.

Share of voice

17%

Of all named tools, what % are you?

Top rival

Scikit-learn

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Scikit-learn · recommended 1×
TensorFlow · recommended 1×
Keras · recommended 1×
PyTorch · recommended 1×
Hugging Face Transformers · recommended 1×

CATEGORY QUERY
What Python library helps detect anomalies across various data types like text and images?
you: #2
AI recommended (in order):
1. Scikit-learn
2. PyOD ← you
3. TensorFlow
4. Keras
5. PyTorch
6. Hugging Face Transformers
7. OpenCV
Show full AI answer
CATEGORY QUERY
Seeking a robust Python framework for unsupervised outlier detection with agentic capabilities.
you: #1
AI recommended (in order):
1. PyOD ← you
2. scikit-learn
3. ADTK
4. DeepOD
5. Alibi Detect
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of yzhao062/pyod?
pass
AI named yzhao062/pyod explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yzhao062/pyod in production, what risks or prerequisites should they evaluate first?
pass
AI named yzhao062/pyod explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo yzhao062/pyod solve, and who is the primary audience?
pass
AI did not name yzhao062/pyod — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of yzhao062/pyod. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/yzhao062/pyod.svg)](https://repogeo.com/en/r/yzhao062/pyod)

HTML

<a href="https://repogeo.com/en/r/yzhao062/pyod"><img src="https://repogeo.com/badge/yzhao062/pyod.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

yzhao062/pyod — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite