RRepoGEO

REPOGEO REPORT · LITE

dmayboroda/minima

Default branch main · commit e6cc03f3 · scanned 6/29/2026, 1:23:05 PM

GitHub: 1,048 stars · 103 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 dmayboroda/minima, 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
    Clarify project identity in README's first sentence

    Why:

    CURRENT
    Minima is an open source RAG on-premises containers, with ability to integrate with ChatGPT and MCP.
    COPY-PASTE FIX
    Minima (mnma.ai) is an open-source, on-premises RAG solution for conversational AI, designed for secure document querying with configurable containers.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://mnma.ai/
  • mediumreadme#3
    Emphasize 'containerized' and 'on-premises' in the README's introductory paragraph

    Why:

    CURRENT
    Minima is an open source RAG on-premises containers, with ability to integrate with ChatGPT and MCP. Minima can also be used as a fully local RAG or with your own deployed LLM.
    COPY-PASTE FIX
    Minima (mnma.ai) is an open-source, on-premises RAG solution for conversational AI, designed for secure document querying with configurable containers. It enables fully local or hybrid RAG capabilities, integrating with various LLMs like Ollama, OpenAI-compatible APIs, Custom GPTs, and Anthropic Claude, ensuring your data remains secure within your infrastructure.

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 dmayboroda/minima
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. Haystack · recommended 2×
  4. Nextcloud · recommended 1×
  5. OpenKM · recommended 1×
  • CATEGORY QUERY
    How can I set up a secure, on-premises RAG system for internal documents?
    you: not recommended
    AI recommended (in order):
    1. Nextcloud
    2. OpenKM
    3. Alfresco Community Edition
    4. Weaviate
    5. Milvus
    6. Qdrant
    7. Sentence-BERT (SBERT) models
    8. OpenAI's `text-embedding-ada-002`
    9. Azure OpenAI Service
    10. Hugging Face Transformers
    11. Llama 2
    12. Mistral 7B / Mixtral 8x7B
    13. Falcon
    14. Vicuna
    15. LangChain
    16. LlamaIndex
    17. Haystack
    18. Kubernetes (K8s)
    19. Docker
    20. Vault (HashiCorp)
    21. Keycloak
    22. NGINX / Apache HTTP Server
    23. SELinux / AppArmor
    24. pfSense
    25. OPNsense

    AI recommended 25 alternatives but never named dmayboroda/minima. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source containerized RAG solutions support various local and cloud LLMs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Rasa
    5. OpenRAG
    6. PrivateGPT
    7. LocalGPT

    AI recommended 7 alternatives but never named dmayboroda/minima. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 dmayboroda/minima?
    pass
    AI named dmayboroda/minima explicitly

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

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

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

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dmayboroda/minima — 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