RRepoGEO

REPOGEO REPORT · LITE

ChaokunHong/MetaScreener

Default branch main · commit a9887d7e · scanned 5/22/2026, 2:27:23 AM

GitHub: 1,312 stars · 48 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
33 /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
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 ChaokunHong/MetaScreener, 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
    Reposition README's opening to explicitly clarify domain

    Why:

    CURRENT
    **MetaScreener** automates the screening phase of systematic reviews using an ensemble of open-source large language models.
    COPY-PASTE FIX
    **MetaScreener** is an open-source, AI-powered tool specifically for automating the screening phase of *systematic reviews* and *evidence synthesis*. It leverages an ensemble of open-source large language models and is *not* intended for drug discovery or virtual screening applications.
  • mediumreadme#2
    Add explicit comparison to systematic review tools in README

    Why:

    COPY-PASTE FIX
    Add to the 'Why MetaScreener?' section or a new 'Comparison' section: 'MetaScreener provides an open-source, transparent, and reproducible alternative to proprietary systematic review screening tools such as Covidence, Rayyan QCRI, DistillerSR, EPPI-Reviewer, and ASReview, focusing on advanced LLM ensemble methods.'
  • lowtopics#3
    Refine topics for better specificity and disambiguation

    Why:

    CURRENT
    deepseek, deepseek-llm, literature-screening, llama4scout, llm, metaanalysis, mistral, pdf-screening, qwen3, screening, screening-api, systematic-review
    COPY-PASTE FIX
    deepseek, deepseek-llm, literature-screening, llama4scout, llm, metaanalysis, mistral, pdf-screening, qwen3, screening-api, systematic-review, evidence-synthesis, literature-review, ai-for-systematic-reviews

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 ChaokunHong/MetaScreener
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Covidence
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Covidence · recommended 1×
  2. Rayyan QCRI · recommended 1×
  3. DistillerSR · recommended 1×
  4. EPPI-Reviewer · recommended 1×
  5. ASReview · recommended 1×
  • CATEGORY QUERY
    How can I automate abstract and PDF screening for systematic reviews using AI?
    you: not recommended
    AI recommended (in order):
    1. Covidence
    2. Rayyan QCRI
    3. DistillerSR
    4. EPPI-Reviewer
    5. ASReview
    6. EndNote
    7. scikit-learn
    8. Python
    9. PyPDF2
    10. pdfminer.six
    11. NLTK
    12. spaCy

    AI recommended 12 alternatives but never named ChaokunHong/MetaScreener. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source tools offer multi-LLM ensemble for robust literature screening decisions?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset/haystack)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. SetFit (huggingface/setfit)
    5. OpenNMT (OpenNMT/OpenNMT-py)
    6. Hugging Face Transformers (huggingface/transformers)

    AI recommended 6 alternatives but never named ChaokunHong/MetaScreener. 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 ChaokunHong/MetaScreener?
    pass
    AI named ChaokunHong/MetaScreener explicitly

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

  • If a team adopts ChaokunHong/MetaScreener in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ChaokunHong/MetaScreener 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 ChaokunHong/MetaScreener solve, and who is the primary audience?
    pass
    AI did not name ChaokunHong/MetaScreener — 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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MARKDOWN (README)
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ChaokunHong/MetaScreener — 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