RRepoGEO

REPOGEO REPORT · LITE

koxudaxi/datamodel-code-generator

Default branch main · commit f6f5fc24 · scanned 5/10/2026, 1:02:13 PM

GitHub: 3,889 stars · 438 forks

AI VISIBILITY SCORE
67 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 koxudaxi/datamodel-code-generator, 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
  • highreadme#1
    Reposition the README H1 and first sentence to highlight comprehensive output types

    Why:

    CURRENT
    # datamodel-code-generator
    
    🚀 Generate Python data models from schema definitions in seconds.
    COPY-PASTE FIX
    # datamodel-code-generator: Generate Python Data Models (Pydantic, Dataclasses, TypedDict) from Schemas & Data
    
    🚀 Generate type-safe Python data models, including Pydantic, dataclasses, and TypedDict, from OpenAPI, JSON Schema, GraphQL, and raw data (JSON/YAML/CSV) in seconds.
  • mediumreadme#2
    Add a clear differentiator statement to the README's 'What it does' section

    Why:

    CURRENT
    ## ✨ What it does
    
    - 📄 Converts **OpenAPI 3**, **JSON Schema**, **GraphQL**, and raw data (JSON/YAML/CSV) into Python models
    COPY-PASTE FIX
    ## ✨ What it does
    
    Unlike specialized tools, `datamodel-code-generator` offers unparalleled flexibility by supporting a wide array of input schemas (OpenAPI, JSON Schema, GraphQL, raw data) and generating diverse Python model types (Pydantic, dataclasses, TypedDict, msgspec) from a single tool.
    
    - 📄 Converts **OpenAPI 3**, **JSON Schema**, **GraphQL**, and raw data (JSON/YAML/CSV) into Python models
  • lowfaq#3
    Add a 'Comparison' or 'Why Choose This?' entry to the FAQ

    Why:

    COPY-PASTE FIX
    ### ❓ How does `datamodel-code-generator` compare to other tools?
    
    Many tools specialize in a single input (e.g., OpenAPI) or output (e.g., Pydantic). `datamodel-code-generator` stands out by providing a unified solution to generate Pydantic, dataclasses, TypedDict, or msgspec models from a comprehensive range of inputs including OpenAPI, JSON Schema, GraphQL, and raw JSON/YAML/CSV data, ensuring maximum flexibility for diverse project needs.

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 koxudaxi/datamodel-code-generator
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
openapi-python-client
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. openapi-python-client · recommended 1×
  2. Pydantic-OpenAPI · recommended 1×
  3. OpenAPI-Spec-Validator · recommended 1×
  4. json-schema-to-python · recommended 1×
  5. Dataclasses-JSON Schema · recommended 1×
  • CATEGORY QUERY
    How to generate Python Pydantic models from an existing OpenAPI specification?
    you: #1
    AI recommended (in order):
    1. datamodel-code-generator ← you
    2. openapi-python-client
    3. Pydantic-OpenAPI
    4. OpenAPI-Spec-Validator
    5. json-schema-to-python
    Show full AI answer
  • CATEGORY QUERY
    Tool to create type-safe Python dataclasses from JSON Schema or CSV files?
    you: not recommended
    AI recommended (in order):
    1. Dataclasses-JSON Schema
    2. Pydantic
    3. JSON Schema to Python (json-schema-to-python)
    4. attrs
    5. cattrs
    6. dataclasses
    7. Cerberus
    8. Voluptuous
    9. pandas

    AI recommended 9 alternatives but never named koxudaxi/datamodel-code-generator. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 koxudaxi/datamodel-code-generator?
    pass
    AI did not name koxudaxi/datamodel-code-generator — likely talking about a different project

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

  • If a team adopts koxudaxi/datamodel-code-generator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named koxudaxi/datamodel-code-generator 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 koxudaxi/datamodel-code-generator solve, and who is the primary audience?
    pass
    AI named koxudaxi/datamodel-code-generator explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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