RRepoGEO

REPOGEO REPORT · LITE

BlazeUp-AI/Observal

Default branch main · commit 85e6c866 · scanned 6/17/2026, 8:11:42 PM

GitHub: 2,082 stars · 444 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
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 BlazeUp-AI/Observal, 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 opening statement to clarify core offering

    Why:

    CURRENT
    A registry and insight platform for portable AI coding agents. Define context once, install it across tools, and learn what works.
    COPY-PASTE FIX
    Observal is an open-source registry and context management platform for portable AI coding agents. Define agent skills and context once, publish them, and install them across your development tools and IDEs.
  • mediumtopics#2
    Add more specific topics for AI agent management and context engineering

    Why:

    CURRENT
    agents, analytics, antigravity, claude-code, cli-tool, codex, cursor, cursor-ai, insights, kiro, large-language-models, mcp, open-source, pi, playground, registry, self-hosted, skills
    COPY-PASTE FIX
    ai-agent-management, agent-orchestration, context-engineering, ai-registry, agents, analytics, claude-code, cli-tool, cursor-ai, insights, large-language-models, mcp, open-source, playground, registry, self-hosted, skills
  • lowabout#3
    Refine the 'About' description to emphasize registry and context management

    Why:

    CURRENT
    Observal is a sandboxed artifactory and analytics platform for your AI development stack. Setup Observal, define the scope and share your Skills, MCPs and Agents.
    COPY-PASTE FIX
    Observal is a sandboxed registry and analytics platform for managing and sharing your AI agent skills, MCPs, and context across your development stack.

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 BlazeUp-AI/Observal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangSmith
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangSmith · recommended 2×
  2. LangChain · recommended 1×
  3. LangServe · recommended 1×
  4. LlamaIndex · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    How can I manage and share AI agent skills and context across different development tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangServe
    3. LangSmith
    4. LlamaIndex
    5. OpenAI Assistants API
    6. Microsoft Semantic Kernel
    7. Agent Protocol
    8. Custom API Gateway / Microservices

    AI recommended 8 alternatives but never named BlazeUp-AI/Observal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide analytics and insights for evaluating AI agent performance and context engineering?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. Arize AI
    3. Weights & Biases
    4. Helicone
    5. Humanloop
    6. Deepchecks

    AI recommended 6 alternatives but never named BlazeUp-AI/Observal. 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 BlazeUp-AI/Observal?
    pass
    AI named BlazeUp-AI/Observal explicitly

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

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