RRepoGEO

REPOGEO REPORT · LITE

huggingface/llm-vscode

Default branch master · commit 06612965 · scanned 6/30/2026, 3:26:49 PM

GitHub: 1,314 stars · 142 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
28 /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
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 huggingface/llm-vscode, 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
    vscode-extension, llm, code-completion, ai-assistant, huggingface, code-attribution, ghost-text
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://marketplace.visualstudio.com/items?itemName=HuggingFace.huggingface-vscode
  • mediumreadme#3
    Emphasize core differentiators in the README's opening

    Why:

    CURRENT
    **llm-vscode** is an extension for all things LLM. It uses **llm-ls** as its backend.
    COPY-PASTE FIX
    **llm-vscode** is an extension for all things LLM, bringing flexible, model-agnostic AI coding assistance to VSCode. Powered by **llm-ls** and the Hugging Face ecosystem, it allows you to choose your preferred model for features like ghost-text completion and code attribution.

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 huggingface/llm-vscode
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 2×
  2. Tabnine · recommended 2×
  3. CodeWhisperer · recommended 1×
  4. Cursor · recommended 1×
  5. Jedi · recommended 1×
  • CATEGORY QUERY
    What are the best AI coding assistants for improving productivity in my editor?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. CodeWhisperer
    4. Cursor
    5. Jedi
    6. Kite

    AI recommended 6 alternatives but never named huggingface/llm-vscode. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I get AI code completion and attribution checks directly within my VSCode environment?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. Codeium
    4. Amazon CodeWhisperer
    5. Cody by Sourcegraph (sourcegraph/cody)

    AI recommended 5 alternatives but never named huggingface/llm-vscode. 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 huggingface/llm-vscode?
    pass
    AI did not name huggingface/llm-vscode — 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 huggingface/llm-vscode in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/llm-vscode 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 huggingface/llm-vscode solve, and who is the primary audience?
    pass
    AI named huggingface/llm-vscode explicitly

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

Embed your GEO score

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MARKDOWN (README)
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huggingface/llm-vscode — 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