RRepoGEO

REPOGEO REPORT · LITE

SynaLinks/synalinks-skills

Default branch main · commit 6e027fac · scanned 6/12/2026, 7:38:14 PM

GitHub: 899 stars · 85 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 SynaLinks/synalinks-skills, 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 core value proposition to the opening

    Why:

    CURRENT
    # Claude Skills for Synalinks

This repository contains skills to use with Claude Code.
    COPY-PASTE FIX
    This repository provides **Claude Skills for Synalinks**, designed to ensure large language models generate correct, idiomatic code for the Synalinks framework. Without these skills, LLMs often mix syntax from other frameworks like Keras or LangChain, leading to plausible but broken code. Synalinks skills specifically guide Claude to produce accurate, framework-compliant outputs.
  • mediumtopics#2
    Add specific topics for LLM guidance and framework-specific code generation

    Why:

    CURRENT
    artificial-intelligence, large-language-models, neuro-symbolic, neuro-symbolic-ai, neurosymbolic
    COPY-PASTE FIX
    artificial-intelligence, large-language-models, neuro-symbolic, neuro-symbolic-ai, neurosymbolic, llm-guidance, code-generation, framework-specific-ai, idiomatic-code, llm-constraint
  • mediumabout#3
    Clarify the repository description to highlight its problem-solving aspect

    Why:

    CURRENT
    Claude skills for Synalinks OSS
    COPY-PASTE FIX
    Ensures large language models generate correct, idiomatic code for the Synalinks framework by providing specific Claude skills.

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 SynaLinks/synalinks-skills
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. OpenAI GPT-4 · recommended 1×
  4. GPT-3.5 Turbo · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to ensure large language models generate correct, idiomatic code for custom frameworks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. GPT-3.5 Turbo
    3. Hugging Face Transformers (huggingface/transformers)
    4. CodeLlama (facebookresearch/codellama)
    5. StarCoder (bigcode-project/starcoder)
    6. DeepSeek Coder (deepseek-ai/deepseek-coder)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Pinecone
    10. Weaviate (weaviate/weaviate)
    11. ChromaDB (chroma-core/chroma)
    12. Pydantic (pydantic/pydantic)
    13. ast module in Python
    14. typescript-eslint (typescript-eslint/typescript-eslint)
    15. Pytest (pytest-dev/pytest)
    16. Jest (jestjs/jest)
    17. NUnit (nunit/nunit)

    AI recommended 17 alternatives but never named SynaLinks/synalinks-skills. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to create standardized, repeatable workflows for complex LLM-driven tasks.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    4. Haystack (deepset-ai/haystack)
    5. OpenAI Assistants API
    6. Prefect (PrefectHQ/prefect)
    7. Airflow (apache/airflow)

    AI recommended 7 alternatives but never named SynaLinks/synalinks-skills. 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 SynaLinks/synalinks-skills?
    pass
    AI named SynaLinks/synalinks-skills explicitly

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

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

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

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SynaLinks/synalinks-skills — 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