RRepoGEO

REPOGEO REPORT · LITE

oughtinc/ice

Default branch main · commit 8fc5d5c3 · scanned 6/15/2026, 6:47:58 PM

GitHub: 568 stars · 73 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 oughtinc/ice, 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
  • highreadme#1
    Clarify README intro to emphasize 'debugger' and 'compositional'

    Why:

    CURRENT
    ICE is a Python library and trace visualizer for language model programs.
    COPY-PASTE FIX
    ICE is a Python library and **debugger** for **compositional** language model programs, offering a trace visualizer to inspect execution.
  • mediumtopics#2
    Add more specific LLM debugging and tracing topics

    Why:

    CURRENT
    debugging, gpt-3, language-model, python
    COPY-PASTE FIX
    debugging, gpt-3, language-model, python, llm-debugging, agent-debugging, trace-visualization, llm-observability
  • lowreadme#3
    Add ICE's core differentiator to the README

    Why:

    COPY-PASTE FIX
    Add a sentence or short paragraph to the README, perhaps in the 'Features' or a new 'Why ICE?' section, stating: 'ICE's core differentiator is providing a structured framework for AI agents to programmatically edit and refine their own prompts in-context during execution.'

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 oughtinc/ice
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weights & Biases
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases · recommended 2×
  2. LangSmith · recommended 2×
  3. LlamaCloud · recommended 2×
  4. OpenTelemetry · recommended 2×
  5. Jaeger · recommended 2×
  • CATEGORY QUERY
    How can I effectively debug complex language model programs and inspect their execution traces?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. LangSmith
    3. LlamaCloud
    4. OpenTelemetry
    5. Jaeger
    6. Honeycomb
    7. PyTorch profiler
    8. TensorBoard
    9. pdb
    10. VS Code Debugger
    11. Python's logging module
    12. Rich

    AI recommended 12 alternatives but never named oughtinc/ice. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help visualize and step through language model agent workflows for better understanding?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. LlamaCloud
    3. LlamaDebug
    4. OpenTelemetry
    5. Jaeger
    6. Zipkin
    7. Weights & Biases
    8. W&B Prompts
    9. Humanloop
    10. Streamlit
    11. Gradio

    AI recommended 11 alternatives but never named oughtinc/ice. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 oughtinc/ice?
    pass
    AI named oughtinc/ice explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite