RRepoGEO

REPOGEO REPORT · LITE

xlang-ai/Spider2

Default branch main · commit 01a4c67c · scanned 6/2/2026, 8:48:21 PM

GitHub: 819 stars · 138 forks

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 xlang-ai/Spider2, 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:

    COPY-PASTE FIX
    text-to-sql, llm-evaluation, nlp-benchmark, semantic-parsing, large-language-models, dataset, iclr-2025, enterprise-sql, sql-generation
  • highreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Spider 2.0 is a cutting-edge benchmark dataset and evaluation framework designed to rigorously assess the performance of Large Language Models (LLMs) on complex, real-world enterprise text-to-SQL workflows. As an ICLR 2025 Oral presentation, it extends the original Spider dataset with enhanced features for more accurate and stable LLM evaluation.
  • mediumabout#3
    Refine the repository's short description for clearer AI parsing

    Why:

    CURRENT
    [ICLR 2025 Oral] Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
    COPY-PASTE FIX
    [ICLR 2025 Oral] Spider 2.0: A benchmark dataset and evaluation framework for LLMs on real-world enterprise Text-to-SQL.

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/Spider2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Spider dataset
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Spider dataset · recommended 1×
  2. sqlflow/sqlflow · recommended 1×
  3. NLTK · recommended 1×
  4. Hugging Face Datasets library · recommended 1×
  5. Label Studio · recommended 1×
  • CATEGORY QUERY
    How can I rigorously evaluate the performance of my text-to-SQL language model?
    you: not recommended
    AI recommended (in order):
    1. Spider dataset
    2. SQLFlow (sqlflow/sqlflow)
    3. NLTK
    4. Hugging Face Datasets library
    5. Label Studio
    6. Prodigy
    7. Amazon Mechanical Turk
    8. Appen
    9. Scale AI
    10. PostgreSQL
    11. MySQL
    12. SQLite
    13. SQL Server
    14. Docker

    AI recommended 14 alternatives but never named xlang-ai/Spider2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust benchmarks for assessing LLM accuracy on complex enterprise SQL generation tasks?
    you: not recommended
    AI recommended (in order):
    1. Spider
    2. WikiSQL
    3. BIRD
    4. SQLova
    5. SParC
    6. CoSQL

    AI recommended 6 alternatives but never named xlang-ai/Spider2. 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 xlang-ai/Spider2?
    pass
    AI named xlang-ai/Spider2 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/Spider2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xlang-ai/Spider2 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/Spider2 solve, and who is the primary audience?
    pass
    AI named xlang-ai/Spider2 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 xlang-ai/Spider2. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/xlang-ai/Spider2.svg)](https://repogeo.com/en/r/xlang-ai/Spider2)
HTML
<a href="https://repogeo.com/en/r/xlang-ai/Spider2"><img src="https://repogeo.com/badge/xlang-ai/Spider2.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

xlang-ai/Spider2 — 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