cc-statistics: AI Coding stats dashboard — track costs, tokens, and efficiency across Claude Code / Gemini CLI / Codex /

by androidZzT · Codex Skill · ★ 118

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install cc-statistics

uv tool install cc-statistics   # or: pipx install cc-statistics
pipx install cc-statistics

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 61/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-codingclaude-codeclicost-estimationdeveloper-toolsgemini-climacos-appmenu-bar-apppythonswiftui

Quick Facts

Stars118
Forks14
LanguageSwift
CategoryCodex Skill
LicenseMIT
Quality Score61/100
Last Updated2026-10-21
Created2026-03-17
Platformsclaude-code, cli, codex, gemini
Est. Tokens~17k

About cc-statistics

cc-statistics The only AI coding tracker that unifies Claude Code, Gemini CLI, Codex CLI, and Cursor — in one native macOS app. Track every token, cost, and session across all your AI tools. 100% local. Zero dependencies. Why? • Features • Screenshots • Quick Start &bul

Compatible Skills

These tools work well together with cc-statistics for enhanced workflows:

  • Usage4Claude — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • TokenEater — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Codex Skill tools