RRepoGEO

REPOGEO REPORT · LITE

skaldlabs/skald

Default branch main · commit 10699624 · scanned 6/13/2026, 9:22:50 AM

GitHub: 556 stars · 40 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 skaldlabs/skald, 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
    Strengthen README's opening paragraph to emphasize self-hosted RAG and knowledge base

    Why:

    CURRENT
    Skald gives you a production-ready RAG in minutes through a plug-and-play API, and then let's you configure your RAG engine exactly to your needs.
    COPY-PASTE FIX
    Skald is an open-source, self-hosted platform that provides a production-ready RAG (Retrieval Augmented Generation) system for your AI applications. It allows you to build and manage a robust knowledge base directly within your infrastructure, offering a plug-and-play API and extensive configuration options.
  • mediumtopics#2
    Add more specific RAG and platform-related topics

    Why:

    CURRENT
    ai, chat, javascript, knowledge-base, python, rag, self-hosted, typescript
    COPY-PASTE FIX
    ai, chat, javascript, knowledge-base, python, rag, self-hosted, typescript, vector-database, llm-ops, ai-platform, information-retrieval
  • lowlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
Skald is released under [Specify License Name(s) here, e.g., a custom license, or a combination of licenses]. Please see the LICENSE file for full details.

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 skaldlabs/skald
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Elasticsearch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Elasticsearch · recommended 1×
  2. ELSER · recommended 1×
  3. deepset-ai/haystack · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. PostgreSQL · recommended 1×
  • CATEGORY QUERY
    How can I set up a production-ready self-hosted RAG system for my AI chatbot?
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch
    2. ELSER
    3. Haystack (deepset-ai/haystack)
    4. LlamaIndex (run-llama/llama_index)
    5. PostgreSQL
    6. pgvector (pgvector/pgvector)
    7. Qdrant (qdrant/qdrant)
    8. Weaviate (weaviate/weaviate)
    9. Milvus (milvus-io/milvus)
    10. Hugging Face Transformers (huggingface/transformers)
    11. sentence-transformers/all-MiniLM-L6-v2
    12. BAAI/bge-large-en-v1.5
    13. FastAPI (tiangolo/fastapi)
    14. Llama 3
    15. Mistral 7B
    16. Mixtral 8x7B
    17. Gemma
    18. vLLM (vllm-project/vllm)
    19. Text Generation Inference (TGI) (huggingface/text-generation-inference)
    20. Ollama (ollama/ollama)
    21. Prometheus (prometheus/prometheus)
    22. Grafana (grafana/grafana)
    23. Logstash
    24. Kibana
    25. Loki (grafana/loki)
    26. Docker
    27. Kubernetes (kubernetes/kubernetes)

    AI recommended 27 alternatives but never named skaldlabs/skald. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source tools for building a RAG knowledge base using Python?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Faiss
    5. Sentence-Transformers
    6. Hugging Face Transformers
    7. Hugging Face Datasets

    AI recommended 7 alternatives but never named skaldlabs/skald. 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 skaldlabs/skald?
    pass
    AI named skaldlabs/skald explicitly

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

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

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

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skaldlabs/skald — 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