RRepoGEO

REPOGEO REPORT · LITE

charmbracelet/catwalk

Default branch main · commit e6762758 · scanned 6/2/2026, 12:41:48 AM

GitHub: 724 stars · 135 forks

AI VISIBILITY SCORE
28 /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
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 charmbracelet/catwalk, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to clearly state LLM focus

    Why:

    CURRENT
    # Catwalk

A database for _Crush_-compatible models.
    COPY-PASTE FIX
    # Catwalk

A collection of LLM inference providers and models, specifically a database for _Crush_-compatible models.
  • mediumreadme#2
    Expand on 'Crush-compatible' in the README

    Why:

    CURRENT
    Is there a provider you’d like to see in Crush? Is there an existing model that needs an update? This is a community-supported project and we welcome and encourge contributions.
    COPY-PASTE FIX
    This database specifically curates providers and models that are compatible with the Charm `Crush` project, enabling seamless integration for your LLM applications. Is there a provider you’d like to see in Crush? Is there an existing model that needs an update? This is a community-supported project and we welcome and encourage contributions.

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 charmbracelet/catwalk
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anyscale Endpoints
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Anyscale Endpoints · recommended 2×
  2. Google Cloud Vertex AI · recommended 2×
  3. Hugging Face Inference API / Inference Endpoints · recommended 1×
  4. OpenAI API · recommended 1×
  5. Microsoft Azure OpenAI Service · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of available large language model inference providers?
    you: not recommended
    AI recommended (in order):
    1. Anyscale Endpoints
    2. Hugging Face Inference API / Inference Endpoints
    3. OpenAI API
    4. Google Cloud Vertex AI
    5. Microsoft Azure OpenAI Service
    6. AWS Bedrock
    7. Replicate

    AI recommended 7 alternatives but never named charmbracelet/catwalk. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source LLM models and their respective inference services?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Together.ai
    3. Anyscale Endpoints
    4. Mixtral 8x7B
    5. Mistral AI API
    6. Perplexity AI
    7. Gemma
    8. Google Cloud Vertex AI
    9. Hugging Face Inference Endpoints
    10. Falcon 180B
    11. AWS SageMaker
    12. Zephyr
    13. vLLM
    14. CodeLlama

    AI recommended 14 alternatives but never named charmbracelet/catwalk. 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 charmbracelet/catwalk?
    pass
    AI did not name charmbracelet/catwalk — 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 charmbracelet/catwalk in production, what risks or prerequisites should they evaluate first?
    pass
    AI named charmbracelet/catwalk 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 charmbracelet/catwalk solve, and who is the primary audience?
    pass
    AI named charmbracelet/catwalk explicitly

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

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charmbracelet/catwalk — 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