RRepoGEO

REPOGEO REPORT · LITE

bird-bench/BIRD-CRITIC-1

Default branch main · commit f408d6c9 · scanned 5/18/2026, 1:57:40 PM

GitHub: 1,095 stars · 34 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 bird-bench/BIRD-CRITIC-1, 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 for LLM evaluation and datasets

    Why:

    COPY-PASTE FIX
    llm, text-to-sql, sql-evaluation, benchmark, dataset, large-language-models, neurips
  • highreadme#2
    Update README H1 to clearly state LLM evaluation purpose

    Why:

    CURRENT
    # BIRD-CRITIC 1.0 (SQL)
    COPY-PASTE FIX
    # BIRD-CRITIC 1.0 (SQL): A Benchmark and Dataset for LLM SQL Evaluation
  • mediumreadme#3
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    BIRD-CRITIC 1.0 (SQL) is a comprehensive benchmark and dataset designed to evaluate Large Language Models (LLMs) on their ability to solve complex, real-world user SQL issues. It provides a robust framework for critiquing LLM-generated SQL and offers high-quality datasets for training and testing text-to-SQL models.

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 bird-bench/BIRD-CRITIC-1
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SQLFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. SQLFlow · recommended 1×
  2. psycopg2 · recommended 1×
  3. mysql-connector-python · recommended 1×
  4. pyodbc · recommended 1×
  5. pandas · recommended 1×
  • CATEGORY QUERY
    How can large language models be evaluated for solving complex user SQL problems?
    you: not recommended
    AI recommended (in order):
    1. SQLFlow
    2. psycopg2
    3. mysql-connector-python
    4. pyodbc
    5. pandas
    6. DBT
    7. SQLancer
    8. Prometheus
    9. Grafana
    10. Datadog
    11. New Relic
    12. Prodigy
    13. Label Studio
    14. Amazon Mechanical Turk

    AI recommended 14 alternatives but never named bird-bench/BIRD-CRITIC-1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets of real-world SQL issues for training LLM assistants?
    you: not recommended
    AI recommended (in order):
    1. SQLShare
    2. Stack Overflow Data Dumps
    3. GitHub
    4. Kaggle
    5. DB-Engines Ranking

    AI recommended 5 alternatives but never named bird-bench/BIRD-CRITIC-1. 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 bird-bench/BIRD-CRITIC-1?
    pass
    AI named bird-bench/BIRD-CRITIC-1 explicitly

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

  • If a team adopts bird-bench/BIRD-CRITIC-1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bird-bench/BIRD-CRITIC-1 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 bird-bench/BIRD-CRITIC-1 solve, and who is the primary audience?
    pass
    AI named bird-bench/BIRD-CRITIC-1 explicitly

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

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bird-bench/BIRD-CRITIC-1 — 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