RRepoGEO

REPOGEO REPORT · LITE

Gabriella439/grace

Default branch main · commit 0160eec5 · scanned 6/9/2026, 6:23:28 PM

GitHub: 599 stars · 43 forks

AI VISIBILITY SCORE
33 /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
2 / 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 Gabriella439/grace, 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 H1 to explicitly state its category and primary use

    Why:

    CURRENT
    # Grace

Grace (short for [Fall-from-Grace](#name)) is a domain-specific programming language for prompting models. In particular, Grace is well-suited for building and auto-generating elaborate prompt chains
    COPY-PASTE FIX
    # Grace: A Functional Programming Language for Advanced Prompt Engineering

Grace is a domain-specific functional programming language explicitly designed for building and auto-generating elaborate prompt chains and workflows with large language models (LLMs).
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Grace differentiates itself from libraries like LangChain and LlamaIndex by offering a dedicated functional programming language for prompt engineering, rather than a Python library. This provides a more structured and type-safe approach to building complex prompt chains and model interactions, focusing on language-level integration for LLMs.
  • mediumreadme#3
    Add a 'Who is Grace for?' section to the README

    Why:

    COPY-PASTE FIX
    ## Who is Grace for?

Grace is ideal for developers, researchers, and prompt engineers who need a robust, type-safe, and declarative way to build, test, and deploy complex prompt chains and AI workflows. If you're looking to move beyond string concatenation and Python libraries for your LLM interactions, Grace offers a powerful alternative.

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 Gabriella439/grace
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. PromptSource · recommended 1×
  4. Pydantic · recommended 1×
  5. OpenAI Function Calling · recommended 1×
  • CATEGORY QUERY
    How can I programmatically generate complex AI prompt chains more effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptSource
    4. Pydantic
    5. OpenAI Function Calling
    6. Google Gemini Function Calling
    7. Haystack
    8. Guidance
    9. Semantic Kernel

    AI recommended 9 alternatives but never named Gabriella439/grace. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a functional language designed for advanced prompt engineering workflows.
    you: not recommended
    AI recommended (in order):
    1. Clojure
    2. Elixir (elixir-lang/elixir)
    3. F# (dotnet/fsharp)
    4. Haskell (ghc/ghc)
    5. Scala (scala/scala)
    6. OCaml (ocaml/ocaml)

    AI recommended 6 alternatives but never named Gabriella439/grace. 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 Gabriella439/grace?
    pass
    AI did not name Gabriella439/grace — likely talking about a different project

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

  • If a team adopts Gabriella439/grace in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Gabriella439/grace 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 Gabriella439/grace solve, and who is the primary audience?
    pass
    AI named Gabriella439/grace explicitly

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

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Gabriella439/grace — 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