RRepoGEO

REPOGEO REPORT · LITE

mlco2/codecarbon

Default branch master · commit 5a1dae40 · scanned 5/16/2026, 9:26:27 AM

GitHub: 1,820 stars · 280 forks

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
    Reposition README H1 and opening sentence to specify software/ML focus

    Why:

    CURRENT
    # Track & reduce CO₂ emissions from your local computing
Estimate and track carbon emissions from your computer, quantify and analyze their impact.
    COPY-PASTE FIX
    # Track & reduce CO₂ emissions from your local software & ML computations
Estimate and track carbon emissions from your software and machine learning workloads running on your local computer, and quantify their environmental impact.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## How CodeCarbon Compares
CodeCarbon focuses on estimating carbon emissions directly from your software and machine learning workloads running on local hardware. Unlike generic hardware monitoring tools (e.g., Intel Power Gadget, AMD uProf) which provide raw power consumption metrics, CodeCarbon translates these into CO₂ emissions and provides a Pythonic API for easy integration into your development workflow. It also complements grid intensity APIs (e.g., Electricity Maps API, WattTime API) by providing the computation-specific usage data, allowing for a more complete picture of your carbon footprint.
  • lowtopics#3
    Add more specific topics for ML and green software

    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-sustainability, green-software

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. Green Algorithms · recommended 1×
  3. AMD uProf · recommended 1×
  4. Electricity Maps API · recommended 1×
  5. WattTime API · recommended 1×
  • CATEGORY QUERY
    How can I monitor and reduce the carbon footprint of my local machine learning computations?
    you: #1
    AI recommended (in order):
    1. CodeCarbon ← you
    2. Green Algorithms
    3. Intel Power Gadget
    4. AMD uProf
    5. Electricity Maps API
    6. WattTime API
    7. NVIDIA Management Library (NVML)
    8. AMD ROCm System Management Interface (SMI)
    9. PyJoules
    10. MobileNet
    11. EfficientNet
    12. Google Cloud
    13. AWS
    14. AWS Cost Explorer
    Show full AI answer
  • CATEGORY QUERY
    What tools help developers measure and optimize energy consumption for sustainable software development?
    you: #3
    AI recommended (in order):
    1. Intel Power Gadget
    2. Scaphandre
    3. CodeCarbon ← you
    4. Green Metrics Tool (GMT)
    5. Perf (Linux Performance Events)
    6. AWS CloudWatch
    7. Azure Monitor
    8. Google Cloud Operations
    9. Joule (by Microsoft Research)
    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