RRepoGEO

REPOGEO REPORT · LITE

openai/automated-interpretability

Default branch main · commit e4aa21bc · scanned 6/25/2026, 10:28:25 AM

GitHub: 1,082 stars · 128 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
23 /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
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 openai/automated-interpretability, 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 description to the 'About' section

    Why:

    COPY-PASTE FIX
    Code and tools for automatically generating, simulating, and scoring explanations of individual neuron behavior in large language models, including a neuron viewer and public datasets.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["interpretability", "llms", "neural-networks", "machine-learning", "ai-interpretability", "neuron-explanation", "deep-learning", "gpt-2"]
  • highreadme#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    Add the following section to your README:

## License

This project is currently released under [SPECIFY LICENSE(S) HERE, e.g., MIT License or Apache 2.0 License]. Please review the terms carefully before use.

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 openai/automated-interpretability
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TransformerLens
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TransformerLens · recommended 1×
  2. Captum · recommended 1×
  3. Neuroscope · recommended 1×
  4. Lucid · recommended 1×
  5. Interpret-DD · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate explanations for individual neuron behavior in large language models?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens
    2. Captum
    3. Neuroscope
    4. Lucid
    5. Interpret-DD
    6. LIME
    7. SHAP

    AI recommended 7 alternatives but never named openai/automated-interpretability. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available to visualize and analyze activations within deep learning models?
    you: not recommended
    AI recommended (in order):
    1. Captum (facebookresearch/captum)
    2. TensorBoard (tensorflow/tensorboard)
    3. Lucid (tensorflow/lucid)
    4. DeepViz
    5. Netron
    6. PyTorchViz
    7. Keras-Vis

    AI recommended 7 alternatives but never named openai/automated-interpretability. 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 openai/automated-interpretability?
    pass
    AI named openai/automated-interpretability explicitly

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

  • If a team adopts openai/automated-interpretability in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/automated-interpretability 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 openai/automated-interpretability solve, and who is the primary audience?
    pass
    AI did not name openai/automated-interpretability — 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?

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openai/automated-interpretability — 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