by tddworks · Codex Skill · ★ 1.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
brew install --cask claudebar
git clone https://github.com/tddworks/ClaudeBar.git
Taken from the project's README on GitHub. Check the security audit before running it.
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.
| Stars | 1,572 |
| Forks | 158 |
| Language | Swift |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 75/100 |
| Open Issues | 17 |
| Last Updated | 2026-11-05 |
| Created | 2025-12-20 |
| Platforms | claude-code, codex, gemini |
| Est. Tokens | ~17k |
ClaudeBar A macOS menu bar application that monitors AI coding assistant usage quotas. Keep track of your Claude, Codex, Gemini, GitHub Copilot, Antigravity, Z.ai, Kimi, Kiro, and Amp usage at a glance. Dark Mode Light Mode CLI Theme<
These tools work well together with ClaudeBar for enhanced workflows:
Looking for a ClaudeBar alternative? If you're comparing ClaudeBar with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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A Model Context Protocol (MCP) that allows Claude Desktop and other AI tools (Claude Code, Cursor, Antigravity
Explore other popular codex skill tools:
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: