RRepoGEO

REPOGEO REPORT · LITE

hkust-nlp/CodeIO

Default branch master · commit 1d3541cc · scanned 6/1/2026, 6:23:35 PM

GitHub: 569 stars · 34 forks

AI VISIBILITY SCORE
28 /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
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 hkust-nlp/CodeIO, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm-reasoning, code-generation, program-synthesis, input-output-prediction, nlp, machine-learning, icml-2025, research-project, dataset
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file in the repository root, choosing an appropriate open-source license like MIT or Apache-2.0, and ensure the README mentions the chosen license.)
  • mediumreadme#3
    Clarify the README introduction to emphasize research and dataset

    Why:

    CURRENT
    CodeI/O is a novel approach that transforms code-based reasoning patterns into natural language formats to enhance Large Language Models' reasoning capabilities.
    COPY-PASTE FIX
    CodeI/O is a novel research approach and dataset (ICML 2025 Oral) designed to enhance Large Language Models' reasoning capabilities by systematically transforming and condensing code-based reasoning patterns into natural language formats.

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 hkust-nlp/CodeIO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Hugging Face Transformers · recommended 1×
  3. OpenAI API · recommended 1×
  4. Google AI Studio · recommended 1×
  5. Vertex AI · recommended 1×
  • CATEGORY QUERY
    How can I improve large language models' code reasoning abilities using input-output examples?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. OpenAI API
    3. Google AI Studio
    4. Vertex AI
    5. Weights & Biases
    6. LangChain
    7. DeepSpeed
    8. FSDP

    AI recommended 8 alternatives but never named hkust-nlp/CodeIO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help translate code-based reasoning patterns into natural language for LLMs?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Semantic Kernel
    5. Guidance
    6. LMQL
    7. OpenAI Function Calling

    AI recommended 7 alternatives but never named hkust-nlp/CodeIO. 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 hkust-nlp/CodeIO?
    pass
    AI named hkust-nlp/CodeIO explicitly

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

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

Embed your GEO score

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hkust-nlp/CodeIO — 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