RRepoGEO

REPOGEO REPORT · LITE

MadcowD/ell

Default branch main · commit 9d129846 · scanned 5/17/2026, 2:43:13 PM

GitHub: 5,869 stars · 346 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 MadcowD/ell, 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's opening statement to clarify purpose and avoid miscategorization

    Why:

    CURRENT
    `ell` is a lightweight, functional prompt engineering framework built on a few core principles:
    COPY-PASTE FIX
    MadcowD/ell's `ell` is a lightweight Python library and functional framework for *language model programming* and *prompt engineering*. It is designed for building and iterating on LLM applications, not for general-purpose programming or hardware management.
  • hightopics#2
    Expand repository topics to include more specific LLM-related terms

    Why:

    CURRENT
    ai, prompt-engineering
    COPY-PASTE FIX
    ai, prompt-engineering, llm, python, generative-ai, language-models, framework
  • mediumreadme#3
    Add a 'What `ell` solves' section to explicitly address key use cases

    Why:

    COPY-PASTE FIX
    ## What `ell` solves

The `ell` library helps developers:
*   Manage and iterate on LLM prompts programmatically.
*   Build structured and reusable prompt engineering components for AI applications.
*   Version and serialize prompts automatically for robust development workflows.

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 MadcowD/ell
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. PromptLayer · recommended 2×
  3. Pydantic · recommended 1×
  4. Jinja2 · recommended 1×
  5. LiteLLM · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight Python library to manage and iterate on LLM prompts programmatically.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pydantic
    3. Jinja2
    4. PromptLayer
    5. LiteLLM
    6. Simple f-strings / Python's `.format()`
    7. Guidance

    AI recommended 7 alternatives but never named MadcowD/ell. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build structured and reusable prompt engineering components for AI applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptLayer
    4. OpenAI API
    5. DSPy
    6. Guardrails AI

    AI recommended 6 alternatives but never named MadcowD/ell. 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 MadcowD/ell?
    pass
    AI named MadcowD/ell explicitly

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

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

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

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MadcowD/ell — 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