RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/kernel-design-agents

Default branch main · commit dda6be3c · scanned 6/17/2026, 12:08:22 AM

GitHub: 597 stars · 49 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mit-han-lab/kernel-design-agents, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    An agent-centric workflow for using coding agents to research, implement, verify, and iterate on performance-sensitive CUDA kernel tasks.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, cuda, kernel-design, gpu-optimization, llm-agents, performance-engineering
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0).

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 mit-han-lab/kernel-design-agents
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym · recommended 1×
  2. RLlib · recommended 1×
  3. TVM · recommended 1×
  4. Halide · recommended 1×
  5. NVIDIA Nsight Compute · recommended 1×
  • CATEGORY QUERY
    How to automate CUDA kernel development and optimization using AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. RLlib
    3. TVM
    4. Halide
    5. NVIDIA Nsight Compute
    6. GPyOpt
    7. Spearmint

    AI recommended 7 alternatives but never named mit-han-lab/kernel-design-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help design and verify high-performance GPU kernels with agent-based workflows?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Toolkit
    2. Nsight Compute
    3. Nsight Systems
    4. AMD ROCm Platform
    5. HIP
    6. ROCm-Profiler
    7. OpenCL
    8. AMD CodeXL
    9. Radeon GPU Analyzer
    10. Intel VTune Amplifier
    11. Kokkos
    12. RAJA
    13. PyTorch
    14. TensorFlow
    15. GDB-CUDA
    16. GDB-ROCm

    AI recommended 16 alternatives but never named mit-han-lab/kernel-design-agents. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 mit-han-lab/kernel-design-agents?
    pass
    AI named mit-han-lab/kernel-design-agents explicitly

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

  • If a team adopts mit-han-lab/kernel-design-agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mit-han-lab/kernel-design-agents 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 mit-han-lab/kernel-design-agents solve, and who is the primary audience?
    pass
    AI named mit-han-lab/kernel-design-agents explicitly

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

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mit-han-lab/kernel-design-agents — 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