RRepoGEO

REPOGEO REPORT · LITE

mlco2/codecarbon

Default branch master · commit 11374f42 · scanned 6/27/2026, 4:51:11 AM

GitHub: 1,867 stars · 296 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)

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

AI VISIBILITY SCORE
89 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
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 mlco2/codecarbon, 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
    Clarify README's opening sentence to specify ML/AI focus

    Why:

    CURRENT
    Estimate and track carbon emissions from your computer, quantify and analyze their impact.
    COPY-PASTE FIX
    Estimate and track carbon emissions from your **local ML/AI computing**, quantify and analyze their impact on the environment.
  • mediumabout#2
    Update repository description to highlight ML/AI focus

    Why:

    CURRENT
    Track emissions from Compute and recommend ways to reduce their impact on the environment.
    COPY-PASTE FIX
    Track carbon emissions from **local ML/AI computing** and recommend ways to reduce their impact on the environment.
  • lowtopics#3
    Add more specific ML/AI related topics

    Why:

    CURRENT
    ai-ethics, carbon-emissions, carbon-footprint, co2-emissions, energy-consumption, energy-efficiency, fairness, sustainability
    COPY-PASTE FIX
    ai-ethics, carbon-emissions, carbon-footprint, co2-emissions, energy-consumption, energy-efficiency, fairness, sustainability, machine-learning, artificial-intelligence, ml-ops, responsible-ai

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 mlco2/codecarbon
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
Intel Power Gadget
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Intel Power Gadget · recommended 2×
  2. Scaphandre · recommended 2×
  3. PowerTOP · recommended 2×
  4. Green Algorithms · recommended 1×
  5. AMD uProf · recommended 1×
  • CATEGORY QUERY
    How can I monitor and reduce the carbon footprint of my local Python scripts?
    you: #1
    AI recommended (in order):
    1. CodeCarbon ← you
    2. Green Algorithms
    3. Intel Power Gadget
    4. AMD uProf
    5. PyJoules
    6. Scaphandre
    7. PowerTOP
    8. NumPy
    9. Pandas
    10. scikit-learn
    11. Google Cloud
    12. Microsoft Azure
    13. AWS
    Show full AI answer
  • CATEGORY QUERY
    What tools help developers measure energy consumption and improve sustainability of their applications?
    you: #3
    AI recommended (in order):
    1. Intel Power Gadget
    2. Scaphandre
    3. CodeCarbon ← you
    4. Green Metrics Tool (GMT)
    5. Kepler
    6. PowerTOP
    7. Azure Carbon Optimization (Preview)
    8. AWS Customer Carbon Footprint Tool
    9. Google Cloud Carbon Footprint
    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 mlco2/codecarbon?
    pass
    AI named mlco2/codecarbon explicitly

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

  • If a team adopts mlco2/codecarbon in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mlco2/codecarbon 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 mlco2/codecarbon solve, and who is the primary audience?
    pass
    AI named mlco2/codecarbon explicitly

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

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mlco2/codecarbon — 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