No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by maksimzayats · MCP Server · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is acodex safe to install? View the security audit →
acodex acodex is a fast, typed Python SDK for Codex CLI workflows (sync/async, streaming, structured output, images, safety controls), and API parity with the TypeScript SDK. What is acodex? acodex spawns the CLI and exchanges JSONL events over stdin/stdout so you can run agent threads from Python with a fully typed surface: sync + async clients, streaming events, structured output, image inputs, resumable threads, and safety controls exposed as explicit options. Install Prerequisite: Codex CLI acodex wraps an external CLI. Install the Codex CLI and ensure is on your (or pass to / ). Upstream CLI: https://github.com/openai/codex One installation option: Install acodex (uv-first) also works, but is recommended. Recommended for structured output: s
| Stars | 57 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.6922545171975/100 |
| Last Updated | 2026-09-21 |
| Created | 2026-02-14 |
| Platforms | cli, codex, mcp, python |
| Est. Tokens | ~14k |
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acodex is Local MCP automation bridge for the Codex desktop app, with CLI, HTTP server, and live codex_app.* tool access.. It is categorized as a MCP Server with 57 GitHub stars.
acodex is primarily written in Python. It covers topics such as agent, ai, automation.
You can find installation instructions and usage details in the acodex GitHub repository at github.com/maksimzayats/acodex. The project has 57 stars and 1 forks, indicating an active community.
acodex is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to acodex on Agent Skills Hub include open-jet, AutoRedTeam-Orchestrator, recall. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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.
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