REPOGEO REPORT · LITE
bird-bench/BIRD-CRITIC-1
Default branch main · commit f408d6c9 · scanned 6/29/2026, 8:17:54 PM
GitHub: 1,095 stars · 34 forks
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.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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.
- hightopics#1Add specific topics to improve categorization
Why:
CURRENT(none)
COPY-PASTE FIXllm-evaluation, text-to-sql, sql-generation, llm-benchmark, database-issues, neurips, error-correction, natural-language-to-sql
- highreadme#2Clarify the README's opening statement to position as an LLM benchmark
Why:
CURRENT# BIRD-CRITIC 1.0 (SQL)
COPY-PASTE FIX# BIRD-CRITIC 1.0 (SQL): A Benchmark for LLM SQL Error Resolution BIRD-CRITIC-1 is a comprehensive benchmark dataset designed to evaluate Large Language Models (LLMs) on their ability to solve real-world user SQL issues and perform error correction in complex, cross-database scenarios.
- mediumreadme#3Add a section highlighting BIRD-CRITIC-1's unique differentiators
Why:
COPY-PASTE FIX## Why BIRD-CRITIC-1? Our Differentiators Unlike existing benchmarks such as Spider or WikiSQL that primarily focus on initial SQL generation accuracy, BIRD-CRITIC-1 specifically evaluates an LLM's capability for **SQL error correction** and resolving complex, real-world user database issues. Our dataset emphasizes debugging and iterative refinement, providing a unique challenge for advanced LLM evaluation.
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.
- huggingface/transformers · recommended 1×
- T5 · recommended 1×
- BART · recommended 1×
- GPT-2 · recommended 1×
- OpenAI GPT-3.5 / GPT-4 API · recommended 1×
- CATEGORY QUERYHow can large language models effectively resolve complex SQL problems from user queries?you: not recommendedAI recommended (in order):
- Hugging Face Transformers (huggingface/transformers)
- T5
- BART
- GPT-2
- OpenAI GPT-3.5 / GPT-4 API
- GPT-4
- Google PaLM 2 / Gemini API
- LangChain (langchain-ai/langchain)
- Llama 2
- LlamaIndex (run-llama/llama_index)
- SQLGlot (tobymao/sqlglot)
- psycopg2 (psycopg/psycopg2)
- mysql.connector (mysql/mysql-connector-python)
- Streamlit (streamlit/streamlit)
- Gradio (gradio-app/gradio)
AI recommended 15 alternatives but never named bird-bench/BIRD-CRITIC-1. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat benchmarks exist for evaluating LLM performance on real-world SQL generation and debugging tasks?you: not recommendedAI recommended (in order):
- Spider
- WikiSQL
- BIRD
- SQL-PaLM
- CodeXGLUE
- NL2SQL
AI recommended 6 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 completenesswarn
Suggestion:
- README presencepass
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?passAI 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?passAI 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?passAI 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?
Embed your GEO score
Drop this badge into the README of bird-bench/BIRD-CRITIC-1. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/bird-bench/BIRD-CRITIC-1)<a href="https://repogeo.com/en/r/bird-bench/BIRD-CRITIC-1"><img src="https://repogeo.com/badge/bird-bench/BIRD-CRITIC-1.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
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