RRepoGEO

REPOGEO REPORT · LITE

charmbracelet/mods

Default branch main · commit 0425d0d7 · scanned 5/20/2026, 4:01:53 AM

GitHub: 4,521 stars · 237 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 charmbracelet/mods, 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 specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    cli, llm, ai, command-line, terminal, pipeline, go
  • highhomepage#2
    Set the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/charmbracelet/mods
  • mediumreadme#3
    Reposition the README's opening sentence to emphasize 'CLI tool'

    Why:

    CURRENT
    AI for the command line, built for pipelines.
    COPY-PASTE FIX
    Mods is a powerful CLI tool for integrating Large Language Models (LLMs) directly into your command-line pipelines.

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/mods
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. openai/openai-python · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. ggerganov/llama.cpp · recommended 1×
  5. abetlen/llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language model capabilities directly into my CLI pipelines?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Python Library (openai/openai-python)
    2. Hugging Face Transformers Library (huggingface/transformers)
    3. LangChain (langchain-ai/langchain)
    4. Llama.cpp (ggerganov/llama.cpp)
    5. llama-cpp-python (abetlen/llama-cpp-python)
    6. Google Cloud Vertex AI SDK for Python (googleapis/python-aiplatform)
    7. Anthropic Python SDK (anthropics/anthropic-sdk-python)

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

    Show full AI answer
  • CATEGORY QUERY
    What tools allow me to process command output with AI and format results easily?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. OpenAI API
    3. Python
    4. Pydantic (pydantic/pydantic)
    5. Claude 3.5 Sonnet
    6. Anthropic API
    7. Instructor (jxnl/instructor)
    8. Google Gemini 1.5 Pro
    9. Google AI Studio
    10. LangChain (langchain-ai/langchain)
    11. Mistral Large
    12. Mistral AI API
    13. JSON Schema
    14. Ollama (ollama/ollama)
    15. Llama 3
    16. regular expressions
    17. PowerShell
    18. Azure OpenAI Service
    19. jq (jqlang/jq)

    AI recommended 19 alternatives but never named charmbracelet/mods. 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/mods?
    pass
    AI named charmbracelet/mods explicitly

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

  • If a team adopts charmbracelet/mods in production, what risks or prerequisites should they evaluate first?
    pass
    AI named charmbracelet/mods 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/mods solve, and who is the primary audience?
    pass
    AI named charmbracelet/mods 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/mods — 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