REPOGEO REPORT · LITE

defog-ai/sql-eval

Default branch main · commit b8333241 · scanned 6/3/2026, 2:32:58 PM

GitHub: 744 stars · 72 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 defog-ai/sql-eval, 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 relevant topics for Text-to-SQL evaluation

Why:

COPY-PASTE FIX

llm, text-to-sql, sql-evaluation, nlp, generative-ai, benchmarking, data-science, machine-learning, ai-models

highreadme#2

Reposition README H1/intro to clearly state its purpose as a Text-to-SQL evaluation framework

Why:

CURRENT

# SQL Generation Evaluation

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. For an in-depth look into our process of creating this evaluation approach, see this.

COPY-PASTE FIX

# SQL Generation Evaluation: A Semantic Benchmarking Framework for LLM-Generated SQL

This repository provides Defog's robust framework for semantically evaluating the accuracy of SQL queries generated by Large Language Models (LLMs). Unlike tools that only parse or generate SQL, `sql-eval` focuses on comparing the *results* of generated SQL against gold-standard queries, ensuring true correctness. It's based on the Spider schema, enhanced with hand-selected questions and queries grouped by category.

mediumhomepage#3
Add a homepage URL to the Defog AI website
Why:
COPY-PASTE FIX
```
https://defog.ai
```

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 defog-ai/sql-eval

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

sqlflow-dev/sqlflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

sqlflow-dev/sqlflow · recommended 1×
tobymao/sqlglot · recommended 1×
psycopg/psycopg2 · recommended 1×
mysql/mysql-connector-python · recommended 1×
sqlite3 · recommended 1×

CATEGORY QUERY
How can I measure the correctness of SQL queries produced by large language models?
you: not recommended
AI recommended (in order):
1. SQLFlow (sqlflow-dev/sqlflow)
2. SQLGlot (tobymao/sqlglot)
3. psycopg2 (psycopg/psycopg2)
4. mysql-connector-python (mysql/mysql-connector-python)
5. sqlite3
6. pandas (pandas-dev/pandas)
7. sqlfluff (sqlfluff/sqlfluff)
8. pgFormatter (darold/pgFormatter)
9. PyTest (pytest-dev/pytest)
AI recommended 9 alternatives but never named defog-ai/sql-eval. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools are available for benchmarking the accuracy of text-to-SQL models?
you: not recommended
AI recommended (in order):
1. Spider Dataset (https://github.com/taoyds/spider)
2. WikiSQL Dataset (https://github.com/salesforce/WikiSQL)
3. Hugging Face `evaluate` library (https://github.com/huggingface/evaluate)
4. SQLFlow (https://github.com/sql-flow/sqlflow)
5. `cx_Oracle` (https://github.com/oracle/python-cx_Oracle)
6. `pyodbc` (https://github.com/mkleehammer/pyodbc)
AI recommended 6 alternatives but never named defog-ai/sql-eval. 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 defog-ai/sql-eval?
pass
AI named defog-ai/sql-eval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts defog-ai/sql-eval in production, what risks or prerequisites should they evaluate first?
pass
AI named defog-ai/sql-eval 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 defog-ai/sql-eval solve, and who is the primary audience?
pass
AI named defog-ai/sql-eval 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 defog-ai/sql-eval. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/defog-ai/sql-eval"><img src="https://repogeo.com/badge/defog-ai/sql-eval.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite