agenttrace-session-audit

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Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

AI & Automation 39,227 stars 6374 forks Updated today MIT

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Skill Content

# agenttrace Session Audit ## Overview Use this skill to inspect local AI coding-agent sessions with [agenttrace](https://github.com/luoyuctl/agenttrace). It focuses on the process behind a run: token and cost spikes, tool failures, retry loops, latency gaps, anomalies, health scores, and session-to-session diffs. agenttrace is local-first and reads session logs from tools such as Claude Code, Codex CLI, Gemini CLI, Aider, Cursor exports, OpenCode, Qwen Code, Kimi, and generic JSON or JSONL traces. ## When to Use This Skill - Use when a user asks why an AI coding run was slow, expensive, shallow, or unreliable. - Use when reviewing local agent logs before retrying a failed or suspicious task. - Use when building a lightweight CI health gate for AI-assisted coding sessions. - Use when comparing two attempts and looking for changed tool paths, retries, or cost patterns. ## How It Works ### Step 1: Discover Available Sessions Prefer an installed `agenttrace` binary when it is available on `PATH`. If the current repository is `luoyuctl/agenttrace`, use `go run ./cmd/agenttrace` instead. ```bash agenttrace --doctor agenttrace --overview ``` If no sessions are detected, report the directories checked by `--doctor` and ask for the exported session file or log directory. ### Step 2: Produce a Human-Readable Audit Use Markdown when the user wants a concise report they can inspect or share. ```bash agenttrace --overview -f markdown -o agenttrace-overview.md ``` In the rep...

Details

Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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