codex-trace: OpenAI Codex CLI session log viewer for JSONL files in ~/.codex/sessions

by PixelPaw-Labs · Codex Skill · ★ 117

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

How to install codex-trace

git clone https://github.com/PixelPaw-Labs/codex-trace.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 73/100

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.

ai-toolscodexcodex-clidesktop-appdeveloper-toolsjsonlllm-toolslog-vieweropenaiopenai-codex

Quick Facts

Stars117
Forks23
LanguageRust
CategoryCodex Skill
LicenseMIT
Quality Score73/100
Last Updated2026-10-31
Created2026-04-25
Platformsbrowser, cli, codex, rust
Est. Tokens~23k

About codex-trace

Codex Trace Codex Trace is an OpenAI Codex CLI session log viewer for local JSONL files stored in . Browse, search, live-tail, and inspect Codex CLI conversations in a native desktop app and web UI. Codex Trace renders Codex CLI JSONL session files as readable turns with tool calls, token counts, timestamps, collaboration chains, and live SSE tailing for ongoing sessions.

codex-trace alternative? Top 6 similar tools

Looking for a codex-trace alternative? If you're comparing codex-trace 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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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