RRepoGEO

REPOGEO REPORT · LITE

Kaelio/ktx

Default branch main · commit 674b58b3 · scanned 6/13/2026, 10:51:24 PM

GitHub: 1,189 stars · 62 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 Kaelio/ktx, 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 and add disambiguation

    Why:

    CURRENT
    <h1 align="center">
  The context layer for data agents
</h1>
    COPY-PASTE FIX
    <h1 align="center">
  Kaelio/ktx: The Context Layer for Data & Analytics Agents (Not Android KTX)
</h1>
  • mediumreadme#2
    Strengthen README intro with semantic layer and business context keywords

    Why:

    CURRENT
    ktx** is a self-improving context layer that teaches agents how to query your warehouse accurately - from approved metric definitions, joinable columns, and business knowledge it builds and maintains for you.
    COPY-PASTE FIX
    **Kaelio/ktx** is a self-improving **semantic context layer** that provides **comprehensive business context** to data and analytics agents. It teaches AI agents how to query your data warehouse accurately, leveraging approved metric definitions, joinable columns, and deep business knowledge it builds and maintains for you.
  • lowtopics#3
    Expand topics with data governance and catalog terms

    Why:

    CURRENT
    agent, agent-skills, agents, ai-agent, ai-agents, analytics, analytics-engineering, business-intelligence, claude, claude-code, claude-skills, codex, context-layer, data-analysis, data-engineering, llm, mcp, memory, semantic-layer, skills
    COPY-PASTE FIX
    agent, agent-skills, agents, ai-agent, ai-agents, analytics, analytics-engineering, business-intelligence, claude, claude-code, claude-skills, codex, context-layer, data-analysis, data-engineering, llm, mcp, memory, semantic-layer, skills, data-governance, data-catalog, business-glossary, metric-store

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 Kaelio/ktx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Looker
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Looker · recommended 2×
  2. dbt-labs/dbt-core · recommended 2×
  3. Alation · recommended 1×
  4. Collibra Data Governance Center · recommended 1×
  5. Atlan · recommended 1×
  • CATEGORY QUERY
    How can I provide comprehensive business context to AI agents for accurate data querying?
    you: not recommended
    AI recommended (in order):
    1. Alation
    2. Collibra Data Governance Center
    3. Atlan
    4. Confluence
    5. Notion
    6. Looker
    7. dbt (Data Build Tool) (dbt-labs/dbt-core)

    AI recommended 7 alternatives but never named Kaelio/ktx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What is the best way to build a semantic layer for LLMs to analyze business data?
    you: not recommended
    AI recommended (in order):
    1. dbt (dbt-labs/dbt-core)
    2. Cube.js (cube-js/cube)
    3. Looker
    4. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. Neo4j (neo4j/neo4j)
    7. Denodo

    AI recommended 7 alternatives but never named Kaelio/ktx. 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 Kaelio/ktx?
    pass
    AI named Kaelio/ktx explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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