RRepoGEO

REPOGEO REPORT · LITE

skypilot-org/skypilot

Default branch master · commit e1deeb55 · scanned 5/13/2026, 3:26:41 PM

GitHub: 9,976 stars · 1,062 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 skypilot-org/skypilot, 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
    SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure.
    COPY-PASTE FIX
    SkyPilot is an open-source framework that unifies and optimizes AI workload management across any cloud or on-prem infrastructure, intelligently leveraging spot instances and diverse GPUs for cost-effective, scalable deep learning.
  • mediumtopics#2
    Add more specific topics related to multi-cloud GPU management and AI cost optimization

    Why:

    CURRENT
    cloud-computing, cloud-management, cost-optimization, deep-learning, distributed-training, gpu, hyperparameter-tuning, job-queue, job-scheduler, llm-serving, llm-training, machine-learning, ml-infrastructure, ml-platform, mlops, multicloud, slurm, spot-instances, tpu
    COPY-PASTE FIX
    cloud-computing, cloud-management, cost-optimization, deep-learning, distributed-training, gpu, hyperparameter-tuning, job-queue, job-scheduler, llm-serving, llm-training, machine-learning, ml-infrastructure, ml-platform, mlops, multicloud, slurm, spot-instances, tpu, gpu-orchestration, multi-cloud-gpu, ai-cost-optimization
  • mediumreadme#3
    Add a 'Why SkyPilot?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why SkyPilot?' or 'SkyPilot vs. X' that briefly outlines its unique advantages over general MLOps platforms or cloud-specific solutions. This section should be placed prominently, perhaps after the initial overview.

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 skypilot-org/skypilot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kubeflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubeflow · recommended 2×
  2. Red Hat OpenShift AI · recommended 1×
  3. MLflow · recommended 1×
  4. Pachyderm · recommended 1×
  5. Domino Data Lab · recommended 1×
  • CATEGORY QUERY
    How can I unify AI workload management across various clouds and on-prem infrastructure efficiently?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow
    2. Red Hat OpenShift AI
    3. MLflow
    4. Pachyderm
    5. Domino Data Lab
    6. Azure Machine Learning
    7. Google Cloud Vertex AI

    AI recommended 7 alternatives but never named skypilot-org/skypilot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for scaling distributed deep learning training on diverse cloud GPUs using spot instances?
    you: not recommended
    AI recommended (in order):
    1. Run:ai
    2. Kubeflow
    3. Ray
    4. AWS Batch
    5. Google Cloud Batch
    6. Azure Batch
    7. Slurm

    AI recommended 7 alternatives but never named skypilot-org/skypilot. 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 skypilot-org/skypilot?
    pass
    AI named skypilot-org/skypilot explicitly

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

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

    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 skypilot-org/skypilot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/skypilot-org/skypilot"><img src="https://repogeo.com/badge/skypilot-org/skypilot.svg" alt="RepoGEO" /></a>
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