RRepoGEO

REPOGEO REPORT · LITE

MadcowD/ell

Default branch main · commit 9d129846 · scanned 6/28/2026, 7:17:46 PM

GitHub: 5,876 stars · 344 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 README opening to explicitly link repo name to project and purpose

    Why:

    CURRENT
    `ell` is a lightweight, functional prompt engineering framework built on a few core principles:
    COPY-PASTE FIX
    The `MadcowD/ell` repository hosts `ell`, a lightweight, functional prompt engineering framework for large language models. It focuses on treating prompts as programs and offers rich tooling for prompt versioning and optimization.
  • mediumtopics#2
    Expand topics with more specific LLM framework terms

    Why:

    CURRENT
    ai, prompt-engineering
    COPY-PASTE FIX
    ai, prompt-engineering, llm-framework, python-library, prompt-optimization, prompt-versioning
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why `ell`?' or '## `ell` vs. other frameworks', that explains `ell`'s unique approach to prompt engineering (prompts as programs, automatic versioning, local store for optimization) compared to general LLM orchestration libraries.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. PromptLayer · recommended 1×
  4. OpenAI Python Library · recommended 1×
  5. Jinja2 · recommended 1×
  • CATEGORY QUERY
    How can I programmatically manage and structure prompts for large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptLayer
    4. OpenAI Python Library
    5. Jinja2
    6. Weights & Biases Prompts
    7. Haystack

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

    Show full AI answer
  • CATEGORY QUERY
    What are some lightweight Python frameworks for building AI applications with prompt templates?
    you: not recommended
    AI recommended (in order):
    1. LiteLLM (BerriAI/litellm)
    2. Instructor (jxnl/instructor)
    3. Pydantic-Chat (pydantic-chat/pydantic-chat)
    4. LangChain Expressive Language (LCEL) (langchain-ai/langchain)
    5. Guidance (microsoft/guidance)
    6. OpenAI Python Client (openai/openai-python)
    7. Jinja2 (pallets/jinja)
    8. Anthropic Python Client (anthropics/anthropic-sdk-python)

    AI recommended 8 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite