RRepoGEO

REPOGEO REPORT · LITE

benchmark-action/github-action-benchmark

Default branch master · commit 86d8bcf4 · scanned 5/25/2026, 7:51:56 AM

GitHub: 1,227 stars · 182 forks

AI VISIBILITY SCORE
33 /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
2 / 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 benchmark-action/github-action-benchmark, 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 clarify its core function

    Why:

    CURRENT
    GitHub Action for Continuous Benchmarking
    COPY-PASTE FIX
    This GitHub Action provides continuous benchmarking, performance regression detection, and historical visualization for your existing code benchmarks directly within GitHub Actions workflows.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    benchmark, ci, github-action
    COPY-PASTE FIX
    benchmark, ci, github-action, performance-regression, code-performance, metrics, visualization
  • lowreadme#3
    Add a 'Why use this action?' section to the README

    Why:

    COPY-PASTE FIX
    ### Why use this action?
Unlike standalone benchmarking tools or load testers, `github-action-benchmark` integrates directly into your CI/CD pipeline to automatically collect, visualize, and monitor performance metrics from your existing code benchmarks, alerting you to regressions before they impact production.

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 benchmark-action/github-action-benchmark
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
golang/tools
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. golang/tools · recommended 2×
  2. grafana/k6 · recommended 1×
  3. apache/jmeter · recommended 1×
  4. LoadRunner Enterprise · recommended 1×
  5. gatling/gatling · recommended 1×
  • CATEGORY QUERY
    How can I automate performance regression detection in my CI/CD pipeline?
    you: not recommended
    AI recommended (in order):
    1. Grafana k6 (grafana/k6)
    2. JMeter (apache/jmeter)
    3. LoadRunner Enterprise
    4. Gatling (gatling/gatling)
    5. Dynatrace
    6. New Relic
    7. Sitespeed.io (sitespeedio/sitespeed.io)

    AI recommended 7 alternatives but never named benchmark-action/github-action-benchmark. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good tool for visualizing Rust and Go benchmark results in GitHub Actions?
    you: not recommended
    AI recommended (in order):
    1. Criterion.rs (bheisler/criterion.rs)
    2. GitHub Pages
    3. actions/upload-pages-artifact (actions/upload-pages-artifact)
    4. actions/deploy-pages (actions/deploy-pages)
    5. peaceiris/actions-gh-pages (peaceiris/actions-gh-pages)
    6. Go benchcmp (golang/tools)
    7. Benchstat (golang/tools)
    8. Grafana (grafana/grafana)
    9. Prometheus (prometheus/prometheus)
    10. GitHub Actions Summary
    11. Python (python/cpython)
    12. Node.js (nodejs/node)
    13. Chart.js (chartjs/Chart.js)
    14. Plotly.js (plotly/plotly.js)

    AI recommended 14 alternatives but never named benchmark-action/github-action-benchmark. 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 benchmark-action/github-action-benchmark?
    pass
    AI did not name benchmark-action/github-action-benchmark — likely talking about a different project

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

  • If a team adopts benchmark-action/github-action-benchmark in production, what risks or prerequisites should they evaluate first?
    pass
    AI named benchmark-action/github-action-benchmark 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 benchmark-action/github-action-benchmark solve, and who is the primary audience?
    pass
    AI named benchmark-action/github-action-benchmark 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 benchmark-action/github-action-benchmark. 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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  • Brand-free category queries5 vs 2 in Lite
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