RRepoGEO

REPOGEO REPORT · LITE

StanfordVL/BEHAVIOR-1K

Default branch main · commit 67ad4908 · scanned 5/24/2026, 4:26:54 AM

GitHub: 1,469 stars · 201 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 StanfordVL/BEHAVIOR-1K, 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
  • highabout#1
    Update the GitHub repository description

    Why:

    CURRENT
    BEHAVIOR-1K: a platform for accelerating Embodied AI research. Join our Discord for support: https://discord.gg/bccR5vGFEx
    COPY-PASTE FIX
    BEHAVIOR-1K: A comprehensive simulation benchmark and platform for embodied AI research, focused on 1,000 realistic household activities and human-centered tasks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent.
  • mediumtopics#3
    Expand GitHub repository topics with more specific keywords

    Why:

    CURRENT
    benchmark, embodied-ai, robotics, simulation
    COPY-PASTE FIX
    benchmark, embodied-ai, robotics, simulation, household-robotics, human-centered-ai, robot-manipulation, long-horizon-tasks

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 StanfordVL/BEHAVIOR-1K
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ALFWorld (ALFRED)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ALFWorld (ALFRED) · recommended 1×
  2. AI2-THOR (Artificial Intelligence 2 Thor) · recommended 1×
  3. VirtualHome · recommended 1×
  4. Habitat (Habitat-Matterport 3D Dataset - HM3D) · recommended 1×
  5. RLBench · recommended 1×
  • CATEGORY QUERY
    What simulation benchmarks exist for evaluating embodied AI agents on realistic household activities?
    you: not recommended
    AI recommended (in order):
    1. ALFWorld (ALFRED)
    2. AI2-THOR (Artificial Intelligence 2 Thor)
    3. VirtualHome
    4. Habitat (Habitat-Matterport 3D Dataset - HM3D)
    5. RLBench
    6. RoboTHOR

    AI recommended 6 alternatives but never named StanfordVL/BEHAVIOR-1K. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive platform to train and test robotic agents performing diverse human-centered tasks.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Isaac Sim
    2. Unity Robotics Hub
    3. Gazebo
    4. CoppeliaSim
    5. PyBullet
    6. RoboSuite

    AI recommended 6 alternatives but never named StanfordVL/BEHAVIOR-1K. 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 StanfordVL/BEHAVIOR-1K?
    pass
    AI named StanfordVL/BEHAVIOR-1K explicitly

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

  • If a team adopts StanfordVL/BEHAVIOR-1K in production, what risks or prerequisites should they evaluate first?
    pass
    AI named StanfordVL/BEHAVIOR-1K 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 StanfordVL/BEHAVIOR-1K solve, and who is the primary audience?
    pass
    AI named StanfordVL/BEHAVIOR-1K 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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StanfordVL/BEHAVIOR-1K — 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