RRepoGEO

REPOGEO REPORT · LITE

defog-ai/sqlcoder

Default branch main · commit de724983 · scanned 5/29/2026, 9:58:14 AM

GitHub: 4,029 stars · 278 forks

AI VISIBILITY SCORE
66 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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/sqlcoder, 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 to the repository

    Why:

    COPY-PASTE FIX
    sql, llm, natural-language-to-sql, text-to-sql, generative-ai, large-language-models, open-source-llm
  • highreadme#2
    Reposition the README's main heading to emphasize its open-source and specialized nature

    Why:

    CURRENT
    # Defog SQLCoder
Defog's SQLCoder is a family of state-of-the-art LLMs for converting natural language questions to SQL queries.
    COPY-PASTE FIX
    # Defog SQLCoder: State-of-the-Art Open-Source LLM for Natural Language to SQL
Defog's SQLCoder is a family of state-of-the-art *open-source* large language models (LLMs) specifically designed for converting natural language questions into SQL queries, outperforming proprietary models in this specialized task.
  • mediumabout#3
    Enhance the repository's About section description to highlight its open-source differentiator

    Why:

    CURRENT
    SoTA LLM for converting natural language questions to SQL queries
    COPY-PASTE FIX
    State-of-the-art open-source LLM for converting natural language questions to SQL queries, outperforming proprietary 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
1 / 2
50% of queries surface defog-ai/sqlcoder
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
ChatGPT / GPT-4 (OpenAI API)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ChatGPT / GPT-4 (OpenAI API) · recommended 1×
  2. Google Gemini (Google Cloud Vertex AI) · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. SQLFlow · recommended 1×
  5. Dataherald · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate SQL queries from natural language descriptions?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT / GPT-4 (OpenAI API)
    2. Google Gemini (Google Cloud Vertex AI)
    3. Hugging Face Transformers
    4. SQLFlow
    5. Dataherald
    6. DB-GPT

    AI recommended 6 alternatives but never named defog-ai/sqlcoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which open-source large language models are best for natural language to SQL generation?
    you: #2
    AI recommended (in order):
    1. Code Llama
    2. SQLCoder ← you
    3. StarCoder / StarCoder2
    4. WizardCoder
    5. Phind-CodeLlama
    6. DeepSeek-Coder
    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/sqlcoder?
    pass
    AI named defog-ai/sqlcoder 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/sqlcoder in production, what risks or prerequisites should they evaluate first?
    pass
    AI named defog-ai/sqlcoder 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/sqlcoder solve, and who is the primary audience?
    pass
    AI named defog-ai/sqlcoder explicitly

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

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defog-ai/sqlcoder — 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