RRepoGEO

REPOGEO REPORT · LITE

FSoft-AI4Code/CodeWiki

Default branch main · commit 06e6d013 · scanned 6/17/2026, 6:36:57 PM

GitHub: 1,254 stars · 197 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
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 FSoft-AI4Code/CodeWiki, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root to clarify usage rights.
  • highreadme#2
    Reposition the README H1 to reflect the product's value proposition

    Why:

    CURRENT
    <h1 align="center">CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases</h1>
    COPY-PASTE FIX
    <h1 align="center">CodeWiki: An AI-Powered Framework for Holistic, Structured Repository Documentation</h1>
  • mediumtopics#3
    Expand repository topics with more specific AI and architecture keywords

    Why:

    CURRENT
    automated-documentation, code-documentation, code-understanding, documentation-generator, open-source-deepwiki, repository-documentation
    COPY-PASTE FIX
    ai-powered-documentation, code-intelligence, large-language-models, llm-applications, software-engineering, architecture-documentation, multilingual-code, automated-code-analysis, automated-documentation, code-documentation, code-understanding, documentation-generator, open-source-deepwiki, repository-documentation

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 FSoft-AI4Code/CodeWiki
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Doxygen
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Doxygen · recommended 1×
  2. sphinx-doc/sphinx · recommended 1×
  3. jsdoc/jsdoc · recommended 1×
  4. OAI/OpenAPI-Specification · recommended 1×
  5. swagger-api/swagger-ui · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate comprehensive, structured documentation for a large codebase?
    you: not recommended
    AI recommended (in order):
    1. Doxygen
    2. Sphinx (sphinx-doc/sphinx)
    3. JSDoc (jsdoc/jsdoc)
    4. Swagger (OAI/OpenAPI-Specification)
    5. Swagger UI (swagger-api/swagger-ui)
    6. OpenAPI Generator (OpenAPITools/openapi-generator)
    7. TypeDoc (TypeStrong/typedoc)
    8. MkDocs (mkdocs/mkdocs)

    AI recommended 8 alternatives but never named FSoft-AI4Code/CodeWiki. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What AI tools exist for generating architecture-aware documentation across multiple programming languages?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. GPT-4 API
    3. Documatic.ai
    4. Swimm.io
    5. Sourcegraph
    6. Cody AI
    7. ArchUnit
    8. PlantUML
    9. Mermaid.js

    AI recommended 9 alternatives but never named FSoft-AI4Code/CodeWiki. 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 FSoft-AI4Code/CodeWiki?
    pass
    AI named FSoft-AI4Code/CodeWiki explicitly

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

  • If a team adopts FSoft-AI4Code/CodeWiki in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FSoft-AI4Code/CodeWiki 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 FSoft-AI4Code/CodeWiki solve, and who is the primary audience?
    pass
    AI named FSoft-AI4Code/CodeWiki explicitly

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

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FSoft-AI4Code/CodeWiki — 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