RRepoGEO

REPOGEO REPORT · LITE

mattzcarey/shippie

Default branch main · commit 85342192 · scanned 5/29/2026, 10:07:30 AM

GitHub: 2,369 stars · 243 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 mattzcarey/shippie, 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 to explicitly state its core function and category

    Why:

    CURRENT
    Shippie uses Large Language Models to review code in your CI/CD pipeline. It should pick up on common issues such as: - Exposed secrets - Slow or inefficient code - Potential bugs or unhandled edge cases
    COPY-PASTE FIX
    Shippie is an AI-powered code review and QA agent designed to integrate directly into your CI/CD pipeline. It leverages Large Language Models to automatically identify common issues like exposed secrets, inefficient code, and potential bugs, helping you ship high-quality code faster.
  • mediumtopics#2
    Add more specific AI/LLM code review topics

    Why:

    CURRENT
    agent, agents, cicd, code-quality, code-review, github, gpt-4, huggingface, mcp, modelcontextprotocol, openai, opensource, qa, qa-automation, quality-assurance
    COPY-PASTE FIX
    agent, agents, ai-code-review, cicd, code-quality, code-review, github, gpt-4, huggingface, llm-code-review, mcp, modelcontextprotocol, openai, opensource, qa, qa-automation, quality-assurance, static-analysis
  • lowreadme#3
    Refine the "Ethos" section to emphasize its AI agent capabilities

    Why:

    CURRENT
    Functions as a human code reviewer, using a small set of optimised tools
    COPY-PASTE FIX
    Functions as an autonomous AI code reviewer, leveraging a small set of optimized tools to mimic human-like analysis.

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 mattzcarey/shippie
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepCode AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepCode AI · recommended 2×
  2. GitHub Copilot Enterprise / GitHub Copilot for Business · recommended 1×
  3. SonarQube · recommended 1×
  4. SonarCloud · recommended 1×
  5. SonarLint · recommended 1×
  • CATEGORY QUERY
    Automate code reviews using AI in my CI/CD pipeline to catch common issues?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise / GitHub Copilot for Business
    2. SonarQube
    3. SonarCloud
    4. SonarLint
    5. DeepCode AI
    6. Snyk Code
    7. Codiga
    8. CodeGuru Reviewer
    9. Pylint
    10. ESLint
    11. RuboCop
    12. GitHub Copilot
    13. Tabnine
    14. Reviewdog

    AI recommended 14 alternatives but never named mattzcarey/shippie. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an LLM-powered tool to scan code for bugs and security vulnerabilities locally?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. GitHub Advanced Security
    3. Continue.dev (continue-dev/continue)
    4. Ollama (ollama/ollama)
    5. LM Studio
    6. CodeGemma
    7. DeepCode AI
    8. Snyk
    9. Semgrep (returntocorp/semgrep)

    AI recommended 9 alternatives but never named mattzcarey/shippie. 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 mattzcarey/shippie?
    pass
    AI named mattzcarey/shippie explicitly

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

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