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 icesixgod · MCP Server · ★ 246
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codex-trajectory safe to install? View the security audit →
Codex Trajectory Read this in 简体中文. Codex Trajectory is a read-only Codex plugin that turns local task logs into a privacy-aware event ledger and interactive timeline. It shows turns, approximate model steps, reasoning summaries, assistant messages, tool timing, subagents, compaction, token usage, and failures without changing the original logs. Install Prerequisites: Codex and uv on macOS, Linux, or Windows. Open a new Codex task so the installed tools and skill are loaded. Then ask: Show the safe trajectory summary for this Codex task. Privacy model Safe summary mode is the default. It returns event names, timing, status, token usage, and bounded summaries while hiding tool inputs, tool outputs, raw record metadata, absolute log paths, Git remotes, base instructions, and encrypted reasoning. Full d
| Stars | 246 |
| Forks | 16 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.2978136431184/100 |
| Last Updated | 2026-09-13 |
| Created | 2026-08-13 |
| Platforms | codex, mcp, python |
| Est. Tokens | ~15k |
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codex-trajectory is Privacy-aware trajectory viewer for local Codex task logs. It is categorized as a MCP Server with 246 GitHub stars.
codex-trajectory is primarily written in Python. It covers topics such as codex, codex-plugin, developer-tools.
You can find installation instructions and usage details in the codex-trajectory GitHub repository at github.com/icesixgod/codex-trajectory. The project has 246 stars and 16 forks, indicating an active community.
codex-trajectory is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codex-trajectory on Agent Skills Hub include hal-9000, mcp-nixos, DeepMCPAgent. 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.
Sources & who's responsible: