RRepoGEO

REPOGEO REPORT · LITE

skypilot-org/skypilot

Default branch master · commit 71a7bf98 · scanned 6/24/2026, 2:27:03 AM

GitHub: 10,202 stars · 1,110 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
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 README opening to emphasize multi-cloud/on-prem AI orchestration

    Why:

    CURRENT
    SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure.
    COPY-PASTE FIX
    SkyPilot is a unified system for running, managing, and scaling AI workloads across *any* infrastructure, including multiple cloud providers (AWS, GCP, Azure, OCI, etc.), Kubernetes, Slurm, and on-prem clusters. It provides a single interface for cost-optimized, portable, and distributed deep learning and LLM training and serving.
  • mediumtopics#2
    Add more specific topics for AI/ML orchestration and platforms

    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
    ai-platform, ml-orchestration, distributed-ml, ai-infrastructure, 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
  • lowreadme#3
    Ensure 'News' section is complete and current

    Why:

    CURRENT
    [Jun 2026] **Announcing SkyPilot Sandboxes**: run untrusted, LLM-generated code on the Kubernetes clusters you already own. **Learn more**, **join early access[May 2026] **How Multiverse doubled their GPU utilization with SkyPilot**: **case study[Apr 2026] Introducing **GPU Compass**: One dashboard to browse, compare pricing, and launch across every GPU cloud. Try it at **gpus.skypilot.co**.
    COPY-PASTE FIX
    Review and complete the 'News' section in the README. Ensure all entries are fully visible, correctly dated (e.g., 2024 instead of 2026 if those are typos), and link correctly. For example, ensure the 'join early access' and 'case study' links are complete and functional.

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
Run:AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Run:AI · recommended 2×
  2. Kubeflow · recommended 1×
  3. Domino Data Lab · recommended 1×
  4. Weights & Biases (W&B) · recommended 1×
  5. Pachyderm · recommended 1×
  • CATEGORY QUERY
    How can I manage and scale deep learning jobs across multiple cloud providers and on-prem?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow
    2. Run:AI
    3. Domino Data Lab
    4. Weights & Biases (W&B)
    5. Pachyderm
    6. Valohai

    AI recommended 6 alternatives but never named skypilot-org/skypilot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified platform for cost-optimized LLM training and distributed ML job scheduling.
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. AWS SageMaker
    3. Google Cloud Vertex AI
    4. Azure Machine Learning
    5. Run:AI
    6. Kubeflow (kubeflow/kubeflow)

    AI recommended 6 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?

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skypilot-org/skypilot — 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