RRepoGEO

REPOGEO REPORT · LITE

masoncl/review-prompts

Default branch main · commit 788b0b19 · scanned 6/15/2026, 8:03:23 AM

GitHub: 845 stars · 123 forks

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 masoncl/review-prompts, 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
    Clarify README opening to distinguish from AI code analysis tools

    Why:

    CURRENT
    AI-assisted code review prompts for Linux kernel, systemd, and iproute development. Works with Claude Code and other AI tools.
    COPY-PASTE FIX
    This repository provides a curated collection of AI-assisted code review prompts, specifically designed for Linux kernel, systemd, and iproute development. Unlike automated code analysis tools, `review-prompts` offers structured guidance for human reviewers using AI assistants like Claude Code to generate more effective and targeted feedback.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-prompts, code-review, linux-kernel, systemd, iproute, developer-tools, generative-ai, claude-code
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/masoncl/review-prompts#readme

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 masoncl/review-prompts
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot Enterprise
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot Enterprise · recommended 1×
  2. GitLab Duo Code Suggestions · recommended 1×
  3. DeepCode AI · recommended 1×
  4. Snyk Code · recommended 1×
  5. CodeGuru Reviewer · recommended 1×
  • CATEGORY QUERY
    How can AI assist with code review for complex system-level projects?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. GitLab Duo Code Suggestions
    3. DeepCode AI
    4. Snyk Code
    5. CodeGuru Reviewer
    6. SonarQube
    7. SonarLint
    8. SonarCloud
    9. Pylint
    10. ESLint
    11. Checkstyle
    12. CodeScene
    13. OpenAI API
    14. GPT-4
    15. GPT-3.5

    AI recommended 15 alternatives but never named masoncl/review-prompts. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What AI tools provide structured prompts for deep C/C++ code analysis and verification?
    you: not recommended
    AI recommended (in order):
    1. CodeQL
    2. Coverity
    3. Polyspace Bug Finder / Polyspace Code Prover
    4. Clang Static Analyzer (llvm/llvm-project)
    5. Cppcheck (danmar/cppcheck)

    AI recommended 5 alternatives but never named masoncl/review-prompts. 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 masoncl/review-prompts?
    pass
    AI named masoncl/review-prompts explicitly

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

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

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

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masoncl/review-prompts — 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