RRepoGEO

REPOGEO REPORT · LITE

xlang-ai/UnifiedSKG

Default branch main · commit 073a52b2 · scanned 6/8/2026, 9:38:03 AM

GitHub: 565 stars · 61 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 xlang-ai/UnifiedSKG, 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 opening to emphasize framework nature

    Why:

    CURRENT
    Code for EMNLP 2022 (oral) paper UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. Please refer to our project page for up-to-date related resources (e.g., papers, code, tools, tutorials) for Structured Knowledge Grounding. Load our checkpoints from HuggingFace Model Hub.
    COPY-PASTE FIX
    UnifiedSKG is a comprehensive framework designed to unify and multi-task 21 Structured Knowledge Grounding (SKG) tasks, transforming them into a text-to-text format for seamless integration with large language models. This repository provides the official implementation from our EMNLP 2022 (oral) paper, offering a systematic approach to SKG research and practical application. Load our checkpoints from HuggingFace Model Hub.
  • mediumtopics#2
    Add topics emphasizing "framework" and "unification"

    Why:

    CURRENT
    data-to-text, fact-verification, huggingface-datasets, huggingface-transformers, multi-task-learning, natural-language-processing, nlp, prompt-learning, pytorch, question-answering, semantic-parsing, structured-knowledge-grounding, text-generation
    COPY-PASTE FIX
    data-to-text, fact-verification, huggingface-datasets, huggingface-transformers, multi-task-learning, natural-language-processing, nlp, nlp-framework, prompt-learning, pytorch, question-answering, semantic-parsing, structured-knowledge-grounding, text-generation, unified-framework, knowledge-grounding-framework
  • lowreadme#3
    Clarify "project page" link in README

    Why:

    CURRENT
    Please refer to our project page for up-to-date related resources (e.g., papers, code, tools, tutorials) for Structured Knowledge Grounding.
    COPY-PASTE FIX
    For up-to-date resources including papers, code, tools, and tutorials for Structured Knowledge Grounding, please refer to our project page at [INSERT_PROJECT_PAGE_URL_HERE].

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 xlang-ai/UnifiedSKG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
T5
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. T5 · recommended 1×
  2. Flan-T5 · recommended 1×
  3. mT5 · recommended 1×
  4. DPR · recommended 1×
  5. BM25 · recommended 1×
  • CATEGORY QUERY
    How to perform question answering over structured knowledge bases using text-to-text models?
    you: not recommended
    AI recommended (in order):
    1. T5
    2. Flan-T5
    3. mT5
    4. DPR
    5. BM25
    6. GPT-3.5
    7. GPT-4
    8. Claude
    9. LLaMA 2
    10. BART
    11. GrailQA
    12. SPARQL-T5
    13. SQL-T5

    AI recommended 13 alternatives but never named xlang-ai/UnifiedSKG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework unifies various natural language processing tasks with structured data grounding?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Rasa (RasaHQ/rasa)
    5. Spark NLP (JohnSnowLabs/spark-nlp)
    6. AllenNLP (allenai/allennlp)

    AI recommended 6 alternatives but never named xlang-ai/UnifiedSKG. 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 xlang-ai/UnifiedSKG?
    pass
    AI named xlang-ai/UnifiedSKG explicitly

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

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

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

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xlang-ai/UnifiedSKG — 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