RRepoGEO

REPOGEO REPORT · LITE

yzhao062/anomaly-detection-resources

Default branch master · commit 5ad16ee8 · scanned 5/19/2026, 7:37:30 PM

GitHub: 9,290 stars · 1,805 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
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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/anomaly-detection-resources, 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
    Reposition README opening to clarify repo type

    Why:

    CURRENT
    Anomaly Detection Learning Resources
.. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg
   :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers
   :alt: GitHub stars

.. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue
   :target: https://github.com/yzhao062/anomaly-detection-resources/network
   :alt: GitHub forks

.. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue
   :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE
   :alt: License

.. image:: https://awesome.re/badge-flat2.svg
   :target: https://awesome.re/badge-flat2.svg
   :alt: Awesome

.. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink
   :target: https://github.com/Minqi824/ADBench
   :alt: Benchmark

`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
(also known as *Anomaly Detection*) is an exciting yet challenging field,...
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of learning resources for Anomaly Detection, encompassing books, academic papers, online courses, datasets, and toolkits. It is actively maintained and regularly updated to include the latest advancements, particularly in LLM and VLM applications. 

`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
(also known as *Anomaly Detection*) is an exciting yet challenging field,...
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/yzhao062/anomaly-detection-resources
  • mediumreadme#3
    Emphasize active curation and modern focus in the README

    Why:

    CURRENT
    This repository collects:

#. Books & Academic Papers 
#. Online Courses and Videos
#. Outlier Datasets
#. Open-source and Commercial Libraries/Toolkits
#. Key Conferences & Journals

**More items will be added t
    COPY-PASTE FIX
    This repository collects:

#. Books & Academic Papers 
#. Online Courses and Videos
#. Outlier Datasets
#. Open-source and Commercial Libraries/Toolkits
#. Key Conferences & Journals

**This list is actively maintained and regularly updated to include the latest advancements in anomaly detection, with a particular focus on applications involving Large Language Models (LLMs) and Vision-Language Models (VLMs).**

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
0 / 2
0% of queries surface yzhao062/anomaly-detection-resources
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scikit-learn
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Scikit-learn · recommended 2×
  2. Elastic Stack · recommended 2×
  3. PyOD · recommended 1×
  4. ADTK · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    Seeking a curated list of papers and tools for modern anomaly detection, including LLM applications.
    you: not recommended
    AI recommended (in order):
    1. PyOD
    2. Scikit-learn
    3. ADTK
    4. Hugging Face Transformers
    5. OpenAI API / Azure OpenAI Service
    6. Deep Anomaly Detection (DAD) Library
    7. Elastic Stack

    AI recommended 7 alternatives but never named yzhao062/anomaly-detection-resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources are available for implementing fraud detection or time-series anomaly detection?
    you: not recommended
    AI recommended (in order):
    1. Apache Flink
    2. Apache Kafka
    3. Elastic Stack
    4. Scikit-learn
    5. PyTorch
    6. TensorFlow
    7. AWS Kinesis
    8. Azure Stream Analytics
    9. Google Cloud Dataflow
    10. Splunk

    AI recommended 10 alternatives but never named yzhao062/anomaly-detection-resources. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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/anomaly-detection-resources?
    pass
    AI did not name yzhao062/anomaly-detection-resources — 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?

  • If a team adopts yzhao062/anomaly-detection-resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yzhao062/anomaly-detection-resources 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/anomaly-detection-resources solve, and who is the primary audience?
    pass
    AI did not name yzhao062/anomaly-detection-resources — 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?

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yzhao062/anomaly-detection-resources — 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
yzhao062/anomaly-detection-resources — RepoGEO report