RRepoGEO

REPOGEO REPORT · LITE

stanford-crfm/helm

Default branch main · commit 54908cf1 · scanned 5/12/2026, 11:56:41 PM

GitHub: 2,786 stars · 384 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 stanford-crfm/helm, 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
  • mediumreadme#1
    Enhance README opening with key features for immediate clarity

    Why:

    CURRENT
    **Holistic Evaluation of Language Models (HELM)** is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.
    COPY-PASTE FIX
    **Holistic Evaluation of Language Models (HELM)** is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible, and transparent evaluation and **benchmarking** of foundation models, including large language models (LLMs) and multimodal models. It provides **standardized datasets**, a **unified model interface**, diverse **metrics**, and a **web leaderboard** for comprehensive comparison.
  • lowreadme#2
    Add a 'Why HELM?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why HELM? 

HELM stands apart from other evaluation tools by offering a truly holistic, reproducible, and transparent framework specifically designed for foundation models, including LLMs and multimodal models. Unlike general MLOps platforms or task-specific evaluation harnesses, HELM provides standardized benchmarks, a unified interface for diverse models, and a comprehensive suite of metrics beyond simple accuracy, all presented through an interactive leaderboard.

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 stanford-crfm/helm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weights & Biases (W&B) Prompts
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases (W&B) Prompts · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. confident-ai/deepeval · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    What are good tools for reproducible and transparent LLM performance evaluation?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B) Prompts
    2. MLflow (mlflow/mlflow)
    3. LangChain (langchain-ai/langchain)
    4. DeepEval (confident-ai/deepeval)
    5. Humanloop
    6. Ragas (explodinggradients/ragas)
    7. Arize AI (Phoenix) (Arize-AI/phoenix)

    AI recommended 7 alternatives but never named stanford-crfm/helm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python framework to holistically benchmark multimodal foundation models using common datasets.
    you: not recommended
    AI recommended (in order):
    1. OpenBEAGLE
    2. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    3. Hugging Face Evaluate
    4. MMBench
    5. TorchMetrics
    6. PyTorch-Lightning
    7. TensorFlow Extended (TFX)

    AI recommended 7 alternatives but never named stanford-crfm/helm. 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 stanford-crfm/helm?
    pass
    AI named stanford-crfm/helm explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite