RRepoGEO

REPOGEO REPORT · LITE

microsoft/poml

Default branch main · commit baad63aa · scanned 5/9/2026, 8:37:16 PM

GitHub: 4,855 stars · 245 forks

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 microsoft/poml, 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 state core purpose and differentiate

    Why:

    CURRENT
    POML (Prompt Orchestration Markup Language) is a novel markup language designed to bring structure, maintainability, and versatility to advanced prompt engineering for Large Language Models (LLMs).
    COPY-PASTE FIX
    POML (Prompt Orchestration Markup Language) is a novel markup language specifically designed for advanced prompt engineering with Large Language Models (LLMs). Unlike general-purpose ML frameworks or policy enforcement tools, POML provides a structured, HTML-like syntax to organize, maintain, and integrate diverse data into complex LLM prompts, addressing challenges like format sensitivity and lack of tooling.
  • highreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## POML vs. Other Prompt Engineering Tools

POML complements existing prompt engineering frameworks by focusing on the declarative definition and structured organization of prompts themselves. While tools like LangChain and LlamaIndex provide programmatic interfaces for building LLM applications and orchestrating chains, POML offers a dedicated markup language to define the *content* and *structure* of individual prompts, making them more maintainable, reusable, and easier to integrate with diverse data sources. It can be used alongside these frameworks to enhance prompt quality and management.
  • mediumabout#3
    Expand the repository's 'About' description

    Why:

    CURRENT
    Prompt Orchestration Markup Language
    COPY-PASTE FIX
    A structured markup language for advanced prompt engineering, designed to bring maintainability, versatility, and seamless data integration to Large Language Model (LLM) applications.

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 microsoft/poml
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. LlamaIndex · recommended 2×
  3. PromptPerfect · recommended 1×
  4. Semantic Kernel · recommended 1×
  5. DSPy · recommended 1×
  • CATEGORY QUERY
    How can I better organize complex LLM prompts for improved maintainability and readability?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptPerfect
    4. Semantic Kernel
    5. DSPy
    6. Pydantic
    7. YAML/JSON

    AI recommended 7 alternatives but never named microsoft/poml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage prompt variations and integrate data for sophisticated LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. PromptLayer
    5. Weights & Biases
    6. Vellum

    AI recommended 6 alternatives but never named microsoft/poml. 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 microsoft/poml?
    pass
    AI named microsoft/poml explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of microsoft/poml. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/microsoft/poml"><img src="https://repogeo.com/badge/microsoft/poml.svg" alt="RepoGEO" /></a>
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microsoft/poml — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite