RRepoGEO

REPOGEO REPORT · LITE

VILA-Lab/ATLAS

Default branch main · commit 7fa0c1de · scanned 6/5/2026, 5:02:43 PM

GitHub: 986 stars · 105 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 VILA-Lab/ATLAS, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening sentence to clarify its specific benchmark focus

    Why:

    CURRENT
    This repository contains resources and research on formulating effective queries and prompts for large language models (LLMs). The primary contribution is the introduction of 26 guiding principles aimed at optimizing interactions with LLMs of various scales, such as LLaMA-1/2, GPT-3.5, and GPT-4.
    COPY-PASTE FIX
    ATLAS is a research benchmark focused on **LLM prompting principles**, providing a principled framework for formulating effective queries and prompts for large language models (LLMs). It introduces 26 guiding principles to optimize interactions with LLMs of various scales, such as LLaMA-1/2, GPT-3.5, and GPT-4.
  • mediumhomepage#2
    Add the paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2312.16171

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 VILA-Lab/ATLAS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. PromptPerfect · recommended 1×
  4. wandb/wandb · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    How to craft more effective and optimized prompts for various large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. PromptPerfect
    4. Weights & Biases (wandb/wandb)
    5. Humanloop

    AI recommended 5 alternatives but never named VILA-Lab/ATLAS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the core principles for designing high-quality instructions when interacting with LLMs?
    you: not recommended
    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 VILA-Lab/ATLAS?
    pass
    AI named VILA-Lab/ATLAS explicitly

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

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

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

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VILA-Lab/ATLAS — 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