RRepoGEO

REPOGEO REPORT · LITE

tw93/Kaku

Default branch main · commit 6b492a81 · scanned 6/22/2026, 2:16:50 AM

GitHub: 5,441 stars · 276 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 tw93/Kaku, 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
    Rephrase README's 'Why' section to avoid miscategorization

    Why:

    CURRENT
    Kaku (書く, かく) is the Japanese word for writing: the act of putting thought into form. A deeply customized fork of WezTerm, built for practical defaults on day one while keeping full Lua customization and a fast, lightweight feel. Part of a trilogy: Kaku (書く) writes code, Waza (技) drills habits, Kami (紙) ships documents. Think of them as a family: Kaku is the dad, Waza the big sister, Kami the little sister.
    COPY-PASTE FIX
    Kaku (書く, かく) is the Japanese word for writing: the act of putting thought into form. It is a deeply customized fork of WezTerm, built for practical defaults on day one while keeping full Lua customization and a fast, lightweight feel, specifically optimized for AI coding workflows.
  • hightopics#2
    Add more specific topics for terminal emulators and AI development

    Why:

    CURRENT
    ai-coding, macos, rust, serial, terminal, terminal-app, terminal-emulator, vibe-coding
    COPY-PASTE FIX
    ai-coding, macos, rust, serial, terminal, terminal-app, terminal-emulator, vibe-coding, ai-development-tools, developer-terminal, wezterm-fork, customizable-terminal
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Your Specific License Name(s) here, e.g., a custom license combining MIT and Apache-2.0]. See the [LICENSE.md](LICENSE.md) file for details.

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 tw93/Kaku
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
iTerm2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. iTerm2 · recommended 1×
  2. Alacritty · recommended 1×
  3. Kitty · recommended 1×
  4. WezTerm · recommended 1×
  5. Hyper · recommended 1×
  • CATEGORY QUERY
    What's a fast terminal emulator optimized for AI development workflows on macOS?
    you: not recommended
    AI recommended (in order):
    1. iTerm2
    2. Alacritty
    3. Kitty
    4. WezTerm
    5. Hyper

    AI recommended 5 alternatives but never named tw93/Kaku. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a highly performant and customizable terminal application for macOS development.
    you: not recommended
    AI recommended (in order):
    1. iTerm2 (gnachman/iTerm2)
    2. Alacritty (alacritty/alacritty)
    3. Kitty (kovidgoyal/kitty)
    4. WezTerm (wez/wezterm)
    5. Hyper (vercel/hyper)
    6. Tabby (Eugeny/tabby)

    AI recommended 6 alternatives but never named tw93/Kaku. 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 tw93/Kaku?
    pass
    AI named tw93/Kaku explicitly

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

  • If a team adopts tw93/Kaku in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tw93/Kaku 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 tw93/Kaku solve, and who is the primary audience?
    pass
    AI named tw93/Kaku explicitly

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

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tw93/Kaku — 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