RRepoGEO

REPOGEO REPORT · LITE

microsoft/genaiscript

Default branch main · commit 58c36595 · scanned 6/20/2026, 1:06:04 PM

GitHub: 2,897 stars · 231 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 microsoft/genaiscript, 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 explicitly define the project as an LLM framework

    Why:

    CURRENT
    Programmatically assemble prompts for LLMs using JavaScript. Orchestrate LLMs, tools, and data in code.
    COPY-PASTE FIX
    GenAIScript is a TypeScript/JavaScript framework for building, testing, and deploying AI applications and agents. It allows you to programmatically assemble prompts for LLMs and orchestrate LLMs, tools, and data in code.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Comparison to other LLM Frameworks', that briefly explains how GenAIScript differentiates itself from LangChain, LlamaIndex, and Semantic Kernel, focusing on its TypeScript-native, structured, and type-safe approach.
  • lowreadme#3
    Expand the 'Examples' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section '## Examples' that links to or briefly showcases more complex, real-world use cases beyond simple prompt templating, demonstrating agent behavior or tool orchestration.

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/genaiscript
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchainjs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchainjs · recommended 1×
  2. run-llama/LlamaIndexTS · recommended 1×
  3. handlebars-lang/handlebars.js · recommended 1×
  4. janl/mustache.js · recommended 1×
  5. colinhacks/zod · recommended 1×
  • CATEGORY QUERY
    How to programmatically build and orchestrate complex LLM prompts using JavaScript?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js (langchain-ai/langchainjs)
    2. LlamaIndex.TS (run-llama/LlamaIndexTS)
    3. Handlebars.js (handlebars-lang/handlebars.js)
    4. Mustache.js (janl/mustache.js)
    5. Zod (colinhacks/zod)
    6. string-template (Matt-Esch/string-template)
    7. Custom JavaScript Functions/Classes

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

    Show full AI answer
  • CATEGORY QUERY
    What framework helps script AI agent behavior and integrate large language models with code?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. Haystack by deepset
    5. AutoGPT

    AI recommended 5 alternatives but never named microsoft/genaiscript. 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/genaiscript?
    pass
    AI named microsoft/genaiscript 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/genaiscript in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/genaiscript 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/genaiscript solve, and who is the primary audience?
    pass
    AI named microsoft/genaiscript explicitly

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

Embed your GEO score

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microsoft/genaiscript — 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