RRepoGEO

REPOGEO REPORT · LITE

justrach/codedb

Default branch main · commit b7c584e8 · scanned 5/29/2026, 11:16:57 PM

GitHub: 1,224 stars · 72 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 justrach/codedb, 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 an explicit clarifying sentence to the README's introduction

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the H3: "This is a specialized code intelligence server designed exclusively for AI agents, not a general-purpose snippet manager or key-value store."
  • mediumreadme#2
    Create a 'Why codedb for AI Agents?' comparison section in the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why codedb for AI Agents?', explaining how it differs from general code search/analysis tools by focusing on AI agent needs (e.g., MCP, specific query types for agents).
  • lowtopics#3
    Expand the topics list with more specific AI agent and code analysis terms

    Why:

    CURRENT
    agentic-ai, agents, anthropic, cursor, gemini, mcp, openai, zig
    COPY-PASTE FIX
    agentic-ai, agents, anthropic, cursor, gemini, mcp, openai, zig, code-analysis-for-ai, ai-agent-tooling, code-intelligence-for-ai

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 justrach/codedb
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyCharm Professional
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyCharm Professional · recommended 1×
  2. microsoft/vscode · recommended 1×
  3. microsoft/vscode-python · recommended 1×
  4. microsoft/pylance-release · recommended 1×
  5. jupyterlab/jupyterlab · recommended 1×
  • CATEGORY QUERY
    What tools provide code understanding and navigation for AI agent development?
    you: not recommended
    AI recommended (in order):
    1. PyCharm Professional
    2. VS Code (microsoft/vscode)
    3. Python extension (microsoft/vscode-python)
    4. Pylance (microsoft/pylance-release)
    5. JupyterLab (jupyterlab/jupyterlab)
    6. GitHub Copilot
    7. GitHub Copilot Chat
    8. Sourcetrail (CoatiSoftware/Sourcetrail)
    9. Understand
    10. Spyder (spyder-ide/spyder)

    AI recommended 10 alternatives but never named justrach/codedb. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can AI agents efficiently query code structure, symbols, and dependencies across repositories?
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph
    2. OpenGrok
    3. Kythe
    4. LSIF
    5. cscope
    6. etags
    7. Tree-sitter

    AI recommended 7 alternatives but never named justrach/codedb. 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 justrach/codedb?
    pass
    AI did not name justrach/codedb — 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 justrach/codedb in production, what risks or prerequisites should they evaluate first?
    pass
    AI named justrach/codedb 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 justrach/codedb solve, and who is the primary audience?
    pass
    AI named justrach/codedb explicitly

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

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justrach/codedb — 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