RRepoGEO

REPOGEO REPORT · LITE

HKUDS/FastCode

Default branch main · commit 590e49ba · scanned 6/28/2026, 9:06:35 AM

GitHub: 2,176 stars · 261 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 HKUDS/FastCode, 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
    code-understanding, ai-powered, large-codebase-analysis, token-efficient, code-analysis, llm-framework, software-architecture, code-intelligence, code-generation
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. For example, if the project is intended to be open source, consider a permissive license like MIT or Apache-2.0, or a copyleft license like GPL-3.0, and populate the file with the chosen license text.
  • mediumabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    FastCode: Accelerating and Streamlining Your Code Understanding
    COPY-PASTE FIX
    FastCode: A token-efficient AI framework for accelerating and streamlining comprehensive code understanding and analysis across large-scale codebases.

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 HKUDS/FastCode
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
sourcegraph/sourcegraph
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. sourcegraph/sourcegraph · recommended 1×
  2. Understand · recommended 1×
  3. github/codeql · recommended 1×
  4. oracle/opengrok · recommended 1×
  5. microsoft/vscode · recommended 1×
  • CATEGORY QUERY
    What are efficient and cost-effective tools for understanding large codebases quickly?
    you: not recommended
    AI recommended (in order):
    1. Sourcegraph (sourcegraph/sourcegraph)
    2. Understand
    3. CodeQL (github/codeql)
    4. OpenGrok (oracle/opengrok)
    5. VS Code (microsoft/vscode)
    6. GitLens (gitkraken/vscode-gitlens)
    7. Code Spell Checker (streetsidesoftware/vscode-spell-checker)
    8. Dependency Cruiser (sverweij/dependency-cruiser)
    9. Java Dependency Viewer (redhat-developer/vscode-java-dependency)
    10. Lattix Architect

    AI recommended 10 alternatives but never named HKUDS/FastCode. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which AI-powered code analysis frameworks offer superior speed and accuracy for developers?
    you: not recommended
    AI recommended (in order):
    1. Snyk Code
    2. SonarQube (SonarSource/sonarqube)
    3. CodeGuru Reviewer
    4. Semgrep (returntocorp/semgrep)
    5. GitHub Copilot
    6. Pylint (pylint-dev/pylint)

    AI recommended 6 alternatives but never named HKUDS/FastCode. 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 HKUDS/FastCode?
    pass
    AI named HKUDS/FastCode explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of HKUDS/FastCode. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/HKUDS/FastCode.svg)](https://repogeo.com/en/r/HKUDS/FastCode)
HTML
<a href="https://repogeo.com/en/r/HKUDS/FastCode"><img src="https://repogeo.com/badge/HKUDS/FastCode.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

HKUDS/FastCode — 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