RRepoGEO

REPOGEO REPORT · LITE

rockbruno/SwiftInfo

Default branch master · commit 7796d700 · scanned 5/26/2026, 10:17:00 AM

GitHub: 1,150 stars · 58 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 rockbruno/SwiftInfo, 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 intro to highlight historical metric tracking and analysis

    Why:

    CURRENT
    SwiftInfo is a CLI tool that extracts, tracks and analyzes metrics that are useful for Swift apps.
    COPY-PASTE FIX
    SwiftInfo is a CLI tool designed to **extract, track, and analyze the evolution of key metrics for iOS apps over time**, storing historical data to identify trends and provide deep insights into code quality and project health.
  • mediumtopics#2
    Add more specific topics related to historical metric tracking and code quality analysis

    Why:

    CURRENT
    analytics, cli, hacktoberfest, ios, swift, tools, xcode
    COPY-PASTE FIX
    analytics, cli, ios, swift, xcode, metrics-tracking, code-quality, historical-analysis, app-size, code-coverage, build-metrics
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison with Alternatives

SwiftInfo focuses on **historical tracking and trend analysis of various iOS project metrics**, differentiating it from tools like SwiftLint (which focuses on code style and linting) or Fastlane (which automates build and deployment workflows). While these tools can complement SwiftInfo, SwiftInfo's strength lies in its ability to provide a longitudinal view of your app's health and evolution.

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 rockbruno/SwiftInfo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
fastlane/fastlane
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. fastlane/fastlane · recommended 5×
  2. realm/SwiftLint · recommended 2×
  3. SonarQube · recommended 1×
  4. Clang Static Analyzer · recommended 1×
  5. danger/danger · recommended 1×
  • CATEGORY QUERY
    Tool to analyze Swift project code quality and build metrics automatically?
    you: not recommended
    AI recommended (in order):
    1. SonarQube
    2. SwiftLint (realm/SwiftLint)
    3. Clang Static Analyzer
    4. Danger (danger/danger)
    5. Fastlane (fastlane/fastlane)
    6. Slather (SlatherOrg/Slather)

    AI recommended 6 alternatives but never named rockbruno/SwiftInfo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to track iOS app size, code coverage, and warning counts via CLI?
    you: not recommended
    AI recommended (in order):
    1. xcodebuild
    2. Fastlane gym (fastlane/fastlane)
    3. Fastlane deliver (fastlane/fastlane)
    4. App Store Connect API (fastlane/fastlane)
    5. AppCode
    6. llvm-cov
    7. Slather (SlatherOrg/slather)
    8. Fastlane scan (fastlane/fastlane)
    9. grep
    10. awk
    11. xcpretty (xcpretty/xcpretty)
    12. SwiftLint (realm/SwiftLint)

    AI recommended 12 alternatives but never named rockbruno/SwiftInfo. 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 rockbruno/SwiftInfo?
    pass
    AI named rockbruno/SwiftInfo explicitly

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

  • If a team adopts rockbruno/SwiftInfo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rockbruno/SwiftInfo 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 rockbruno/SwiftInfo solve, and who is the primary audience?
    pass
    AI named rockbruno/SwiftInfo 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 rockbruno/SwiftInfo. 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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rockbruno/SwiftInfo — 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