RRepoGEO

REPOGEO REPORT · LITE

mm7894215/TokenTracker

Default branch main · commit fefd9061 · scanned 6/16/2026, 8:31:44 PM

GitHub: 720 stars · 73 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 mm7894215/TokenTracker, 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
  • highabout#1
    Reposition the repository description to clarify its category and differentiation

    Why:

    CURRENT
    Track token usage across 22 AI coding tools (Claude Code, Codex, Cursor, Gemini, Roo Code, Zed Agent, Goose, and more) — local-first, zero-config, with a beautiful dashboard, native macOS menu bar app, and 4 desktop widgets.
    COPY-PASTE FIX
    TokenTracker is a local-first, zero-config desktop application and CLI for tracking token usage and costs across 22 AI coding tools (Claude Code, Codex, Cursor, Gemini, etc.). Unlike cloud dashboards or general observability platforms, it provides a private, aggregated view of your AI spending with a beautiful dashboard and native macOS app.
  • mediumtopics#2
    Refine repository topics for better categorization

    Why:

    CURRENT
    ai, ai-agent, ai-tools, antigravity, claude-code, cli, codex-cli, cost-tracker, cursor, dashboard, developer-tools, gemini-cli, llm, local-first, macos, nodejs, npm-package, observability, privacy-first, token-tracker
    COPY-PASTE FIX
    ai, ai-agent, ai-tools, claude-code, cli, codex-cli, cost-tracker, cursor, dashboard, developer-tools, gemini-cli, llm, local-first, macos, nodejs, npm-package, observability, privacy-first, token-tracker, ai-cost-management, llm-cost-tracking, developer-productivity
  • mediumreadme#3
    Add a clear positioning statement to the README's introduction

    Why:

    COPY-PASTE FIX
    TokenTracker is a local-first, zero-config desktop application and CLI for tracking token usage and costs across 22 AI coding tools. Unlike cloud dashboards or general observability platforms, it provides a private, aggregated view of your AI spending.

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 mm7894215/TokenTracker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BerriAI/litellm
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BerriAI/litellm · recommended 2×
  2. OpenAI Dashboard · recommended 1×
  3. Azure OpenAI Studio · recommended 1×
  4. Google Cloud AI Platform · recommended 1×
  5. Vertex AI · recommended 1×
  • CATEGORY QUERY
    How can I monitor my token usage and costs across various AI coding assistants?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Dashboard
    2. Azure OpenAI Studio
    3. Google Cloud AI Platform
    4. Vertex AI
    5. Anthropic Console
    6. CloudHealth by VMware
    7. Apptio Cloudability
    8. Finout
    9. Anyscale
    10. Vellum AI
    11. Langfuse
    12. LiteLLM (BerriAI/litellm)

    AI recommended 12 alternatives but never named mm7894215/TokenTracker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a local-first tool to track LLM token consumption across multiple developer AI agents.
    you: not recommended
    AI recommended (in order):
    1. Langfuse (langfuse/langfuse)
    2. OpenTelemetry (open-telemetry/opentelemetry-specification)
    3. Jaeger (jaegertracing/jaeger)
    4. Prometheus (prometheus/prometheus)
    5. Grafana (grafana/grafana)
    6. LiteLLM (BerriAI/litellm)
    7. MLflow (mlflow/mlflow)

    AI recommended 7 alternatives but never named mm7894215/TokenTracker. 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 mm7894215/TokenTracker?
    pass
    AI named mm7894215/TokenTracker explicitly

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

  • If a team adopts mm7894215/TokenTracker in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mm7894215/TokenTracker 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 mm7894215/TokenTracker solve, and who is the primary audience?
    pass
    AI named mm7894215/TokenTracker explicitly

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

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mm7894215/TokenTracker — 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