pinclaw: A wearable terminal to dispatch tasks to your own AI agents — Claude Code, OpenClaw, Codex

by pinclaw-ai · Codex Skill · ★ 91

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How to install pinclaw

npm install -g pinclaw-bridge

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 68/100

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. Full audit →

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-agentai-wearableclaude-codecodexhardwarenrf52840open-sourceopenclawvoice-assistantwearable

Quick Facts

Stars91
Forks9
LanguageC
CategoryCodex Skill
LicenseMIT
Quality Score68/100
Last Updated2026-06-28
Created2026-03-10
Platformsclaude-code, codex
Est. Tokens~16k

About pinclaw

Pinclaw A wearable terminal for your own AI agents. Talk to your Claude Code, OpenClaw, or Codex anytime, anywhere — hands-free, no phone, no app. Website · App Store · Docs · Discord · Twitter You already have powerful agents — Claude Code, OpenClaw, Codex. They're stuck at your desk.

Compatible Skills

These tools work well together with pinclaw for enhanced workflows:

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  • hue — semantic(0.18)+complementary+similar_pop+shared_platform (46%)
  • symbiotic-ai — semantic(0.18)+complementary+similar_pop+shared_platform (46%)
  • ai-first-toolkit — semantic(0.17)+complementary+similar_pop+shared_platform (46%)

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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