RRepoGEO

REPOGEO REPORT · LITE

chiphuyen/sniffly

Default branch main · commit a237d7e9 · scanned 5/15/2026, 6:02:37 PM

GitHub: 1,233 stars · 113 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 chiphuyen/sniffly, 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 sentence to explicitly state its purpose and audience

    Why:

    CURRENT
    Analyze Claude Code logs to help you use Claude Code better.
    COPY-PASTE FIX
    Sniffly is a dedicated analytics dashboard for developers using Claude Code, providing usage stats, error analysis, and sharable message history to optimize your AI coding assistant experience.
  • hightopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    agent, ai, analytics, coding
    COPY-PASTE FIX
    claude-code, llm-observability, ai-coding-assistant, developer-tools, usage-analytics, error-analysis
  • mediumreadme#3
    Add a 'Why Sniffly?' or 'Comparison' section to clarify differentiation

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps titled 'Why Sniffly?' or 'Comparison', with text like: 'Sniffly is purpose-built for Claude Code users. While general LLM observability platforms like LangSmith or Weights & Biases offer broad monitoring, Sniffly focuses specifically on the unique usage patterns, error types, and message histories generated by Claude Code, providing tailored insights for developers.'

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 chiphuyen/sniffly
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangSmith
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangSmith · recommended 1×
  2. Arize AI · recommended 1×
  3. Weights & Biases Prompts · recommended 1×
  4. Grafana · recommended 1×
  5. Prometheus · recommended 1×
  • CATEGORY QUERY
    Need a dashboard to track AI coding assistant usage, errors, and message history.
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. Arize AI
    3. Weights & Biases Prompts
    4. Grafana
    5. Prometheus
    6. Elasticsearch

    AI recommended 6 alternatives but never named chiphuyen/sniffly. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good analytics tools for understanding large language model code generation performance?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow
    3. Comet ML
    4. Deepchecks
    5. HumanEval
    6. Hugging Face evaluate
    7. Streamlit
    8. Gradio
    9. Dash

    AI recommended 9 alternatives but never named chiphuyen/sniffly. 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 chiphuyen/sniffly?
    pass
    AI named chiphuyen/sniffly explicitly

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

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

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

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chiphuyen/sniffly — 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