RRepoGEO

REPOGEO REPORT · LITE

HKUDS/AI-Researcher

Default branch main · commit f9a6f848 · scanned 7/1/2026, 12:23:03 AM

GitHub: 5,513 stars · 692 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)

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

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 HKUDS/AI-Researcher, 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 the project's core identity and scope in the README's opening

    Why:

    CURRENT
    Welcome to **AI-Researcher**🤗 AI-Researcher introduces a revolutionary breakthrough in **Automated Scientific Discovery**🔬, presenting a new system that fundamentally **Reshapes the Traditional Research Paradigm**.
    COPY-PASTE FIX
    Welcome to **AI-Researcher**🤗, a production-ready system for **Autonomous Scientific Discovery** powered by cutting-edge AI agents. It fundamentally **Reshapes the Traditional Research Paradigm** by automating the entire research workflow from concept to publication, distinguishing itself from lab management or general data analysis platforms.
  • hightopics#2
    Expand repository topics to improve categorization

    Why:

    CURRENT
    ai-researcher
    COPY-PASTE FIX
    ai-agent, autonomous-ai, scientific-discovery, research-automation, large-language-models, llm-applications, generative-ai, neurips, scientific-innovation, paper-generation
  • highlicense#3
    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 with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0). If a custom license applies, ensure it is clearly stated in the file.

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 HKUDS/AI-Researcher
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Benchling
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Benchling · recommended 1×
  2. Labguru · recommended 1×
  3. OpenTrons · recommended 1×
  4. Synthego · recommended 1×
  5. Dataiku · recommended 1×
  • CATEGORY QUERY
    Seeking platforms to fully automate the scientific research process from ideation to publication.
    you: not recommended
    AI recommended (in order):
    1. Benchling
    2. Labguru
    3. OpenTrons
    4. Synthego
    5. Dataiku
    6. Alteryx
    7. Overleaf
    8. ResearchGate
    9. Academia.edu

    AI recommended 9 alternatives but never named HKUDS/AI-Researcher. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best AI-powered systems for autonomous scientific discovery and paper generation?
    you: not recommended
    AI recommended (in order):
    1. IBM RXN for Chemistry
    2. AlphaFold
    3. GPT-4
    4. Galactica
    5. ChemOS
    6. EvoGen
    7. Semantic Scholar

    AI recommended 7 alternatives but never named HKUDS/AI-Researcher. 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 HKUDS/AI-Researcher?
    pass
    AI named HKUDS/AI-Researcher explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of HKUDS/AI-Researcher. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HKUDS/AI-Researcher — 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