RRepoGEO

REPOGEO REPORT · LITE

robusta-dev/krr

Default branch main · commit 8ca245f7 · scanned 6/25/2026, 1:16:58 PM

GitHub: 4,626 stars · 277 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 robusta-dev/krr, 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
    Add a high-level positioning statement to the README intro

    Why:

    COPY-PASTE FIX
    Robusta KRR (Kubernetes Resource Recommender) is a powerful CLI tool for **optimizing resource allocation** in Kubernetes clusters. It provides advanced, configurable, multi-algorithm recommendations for CPU and memory requests and limits, offering a more flexible and data-driven alternative to native tools like VPA and Goldilocks.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://robusta.dev/krr
  • lowtopics#3
    Refine topics for better specificity

    Why:

    CURRENT
    cost-control, cost-saving, finops, kubectl, kubernetes, metrics, monitoring, prometheus, rightsizing, vpa
    COPY-PASTE FIX
    cost-control, cost-saving, finops, kubectl, kubernetes, kubernetes-resource-recommendations, metrics, monitoring, prometheus, rightsizing, vpa, workload-rightsizing

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 robusta-dev/krr
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kubecost
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubecost · recommended 2×
  2. Kubernetes Vertical Pod Autoscaler (VPA) · recommended 1×
  3. Kubernetes Horizontal Pod Autoscaler (HPA) · recommended 1×
  4. Goldilocks · recommended 1×
  5. Kube-resource-report · recommended 1×
  • CATEGORY QUERY
    How can I optimize Kubernetes resource allocation based on Prometheus metrics for cost savings?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes Vertical Pod Autoscaler (VPA)
    2. Kubernetes Horizontal Pod Autoscaler (HPA)
    3. Goldilocks
    4. Kube-resource-report
    5. Prometheus
    6. Grafana Dashboards
    7. Kubecost
    8. OpenCost

    AI recommended 8 alternatives but never named robusta-dev/krr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide Kubernetes pod rightsizing recommendations to improve performance and efficiency?
    you: not recommended
    AI recommended (in order):
    1. Kubecost
    2. Prometheus (prometheus/prometheus)
    3. Grafana (grafana/grafana)
    4. Kube-state-metrics (kubernetes/kube-state-metrics)
    5. Goldilocks (FairwindsOps/goldilocks)
    6. Datadog
    7. New Relic
    8. Dynatrace

    AI recommended 8 alternatives but never named robusta-dev/krr. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 robusta-dev/krr?
    pass
    AI named robusta-dev/krr explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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