claude-code-wakatime: Track how much time you spend using Claude Code to AI-code

by wakatime · Claude Skill · ★ 98

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

Security audit verdict: SAFE · quality 70/100

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.

claudeclaude-aiclaude-codeclaude-code-pluginclaude-code-pluginslines-of-code

Quick Facts

Stars98
Forks17
LanguageTypeScript
CategoryClaude Skill
LicenseBSD-3-Clause
Quality Score70/100
Open Issues1
Last Updated2026-10-30
Created2025-08-06
Platformsclaude-code, node
Est. Tokens~14k

About claude-code-wakatime

WakaTime for Claude Code Track your AI usage, lines of code generated, time spent prompting AI, etc. using the WakaTime plugin for Claude Code. WakaTime is an open source plugin for metrics, insights, and time tracking automatically generated from your programming activity.

Compatible Skills

These tools work well together with claude-code-wakatime for enhanced workflows:

  • Claude-Zeroclaw — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (64%)
  • verify — semantic(0.61)+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • claude-shortcuts — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (59%)
  • ai-maestro — semantic(0.24)+complementary+rare_topics+same_lang+similar_pop+shared_platform (58%)

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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:

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