CodexFlow: Unified desktop workbench for AI coding agents across Windows and WSL

by lulu-sk · Codex Skill · ★ 86

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

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.

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.

aianthropicantigravityantigravity-cliclaudeclaude-codecodexcodex-clicodex-cli-guicodex-cli-ui

Quick Facts

Stars86
Forks7
LanguageTypeScript
CategoryCodex Skill
LicenseApache-2.0
Quality Score68/100
Open Issues2
Last Updated2026-10-18
Created2025-10-11
Platformsclaude-code, cli, codex, gemini, node
Est. Tokens~18k

About CodexFlow

CodexFlow A "Unified Workbench" crafted for AI Coding Agents — switch between Codex / Claude / Gemini (and custom engines) in one click, organize sessions and history by project directories, run parallel tasks with Git Worktree, browse and resume conversations with Markdown rendering, and provide a graphical input box for CLI with support for pasting images / dragging files / @project files /...

Compatible Skills

These tools work well together with CodexFlow for enhanced workflows:

  • codexia — semantic(0.57)+complementary+rare_topics+same_lang+shared_platform (64%)
  • codex-mcp-server — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)
  • codex-specialized-subagents — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)

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    This python program allows you to use Claude Code with Google's Gemini models.

  • ai-workflow by nicepkg · ⭐ 283

    🚀 170+ pre-built skills for Claude Code, Cursor, Codex & 14+ AI tools. Stop re-teaching your AI the same thin

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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