RRepoGEO

REPOGEO REPORT · LITE

tontinton/maki

Default branch main · commit 533669b3 · scanned 6/20/2026, 7:02:18 PM

GitHub: 565 stars · 50 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 tontinton/maki, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agent, coding-assistant, llm, generative-ai, token-efficiency, tui, developer-tools
  • highreadme#2
    Strengthen the README's opening statement about its core purpose

    Why:

    CURRENT
    An AI coding agent optimized for minimal use of context tokens, while providing a great user experience.
    COPY-PASTE FIX
    Maki is an **efficient AI coding agent** designed for developers, specifically optimized for minimal context token usage and a superior terminal user experience. It helps you write, debug, and refactor code with intelligent, context-aware assistance.
  • mediumreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Maki stands out from other AI coding assistants like Cursor, Codeium, or GitHub Copilot by focusing on extreme token efficiency, a blazing-fast TUI experience, and a modular tool-based architecture that keeps your context window clean. Unlike cloud-dependent solutions, Maki emphasizes local control and performance, making it ideal for developers who prioritize speed and resource optimization.

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 tontinton/maki
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Cursor
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Cursor · recommended 2×
  2. Codeium · recommended 1×
  3. Tabnine · recommended 1×
  4. GitHub Copilot · recommended 1×
  5. JetBrains AI Assistant · recommended 1×
  • CATEGORY QUERY
    What AI coding assistant minimizes token usage for efficient development workflows?
    you: not recommended
    AI recommended (in order):
    1. Cursor
    2. Codeium
    3. Tabnine
    4. GitHub Copilot
    5. JetBrains AI Assistant

    AI recommended 5 alternatives but never named tontinton/maki. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an AI agent that can parse code and execute tasks without polluting context.
    you: not recommended
    AI recommended (in order):
    1. Cursor
    2. GPT-Engineer (gpt-engineer-org/gpt-engineer)
    3. Smol-Developer (smol-ai/smol-developer)
    4. AutoGPT (Significant-Gravitas/AutoGPT)
    5. Devin
    6. Open Interpreter (OpenInterpreter/Open-Interpreter)
    7. aider (paul-gauthier/aider)

    AI recommended 7 alternatives but never named tontinton/maki. 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 tontinton/maki?
    pass
    AI named tontinton/maki explicitly

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

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

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

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tontinton/maki — 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