RRepoGEO

REPOGEO REPORT · LITE

kLabUM/rrcf

Default branch master · commit 1795a1b4 · scanned 6/11/2026, 9:37:08 AM

GitHub: 523 stars · 119 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 kLabUM/rrcf, 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 the README's opening sentence to highlight core differentiators

    Why:

    CURRENT
    Implementation of the *Robust Random Cut Forest Algorithm* for anomaly detection by Guha et al. (2016).
    COPY-PASTE FIX
    rrcf is a Python implementation of the Robust Random Cut Forest (RRCF) algorithm, specifically designed for efficient and robust anomaly detection in real-time streaming data with constant memory and time updates.
  • mediumreadme#2
    Add a 'Key Differentiators' or 'Why RRCF?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Key Differentiators' or 'Why RRCF?' that explicitly outlines how rrcf stands out from other anomaly detection libraries, particularly for streaming data, high dimensionality, and robustness, potentially mentioning its constant memory/time updates.
  • lowreadme#3
    Enhance the 'About' section with a concise problem statement

    Why:

    CURRENT
    The *Robust Random Cut Forest* (RRCF) algorithm is an ensemble method for detecting outliers in streaming data.
    COPY-PASTE FIX
    The *Robust Random Cut Forest* (RRCF) algorithm addresses the critical challenge of detecting anomalies in complex, high-dimensional streaming data where traditional methods often fail due to memory constraints, computational cost, or sensitivity to noise and duplicates. RRCF is an ensemble method for detecting outliers in streaming data and offers a number of features that many competing anomaly detection algorithms lack.

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 kLabUM/rrcf
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. PyOD · recommended 2×
  3. River · recommended 2×
  4. SciPy · recommended 1×
  5. Pandas · recommended 1×
  • CATEGORY QUERY
    How to detect outliers in real-time data streams using a robust machine learning approach?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. PyOD
    3. River
    4. SciPy
    5. Pandas

    AI recommended 5 alternatives but never named kLabUM/rrcf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python solution for robust anomaly detection in streaming data with duplicate handling.
    you: not recommended
    AI recommended (in order):
    1. PyOD
    2. scikit-learn
    3. River
    4. ADTK
    5. TensorFlow Extended (TFX)
    6. Apache Beam
    7. TensorFlow
    8. Faiss

    AI recommended 8 alternatives but never named kLabUM/rrcf. This is the gap to close.

    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 kLabUM/rrcf?
    pass
    AI named kLabUM/rrcf explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts kLabUM/rrcf in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kLabUM/rrcf 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 kLabUM/rrcf solve, and who is the primary audience?
    pass
    AI named kLabUM/rrcf explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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kLabUM/rrcf — 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