agenttrace-session-audit

name	agenttrace-session-audit
description	Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
category	development
risk	safe
source	community
source_repo	luoyuctl/agenttrace
source_type	community
date_added	2026-05-10
author	luoyuctl
tags	["ai-coding","observability","cost-tracking","session-analysis"]
tools	["claude","cursor","gemini","codex-cli"]
license	MIT
license_source	https://github.com/luoyuctl/agenttrace/blob/master/LICENSE

agenttrace Session Audit

Overview

Use this skill to inspect local AI coding-agent sessions with 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.

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.

agenttrace --overview -f markdown -o agenttrace-overview.md

In the report, lead with the highest-risk sessions and explain why they matter: critical anomalies, repeated tool failures, token or cost waste, long latency gaps, low health scores, and suspiciously shallow sessions.

Step 3: Inspect One Session or Directory

Use the latest session for a quick check, or pass an explicit export path when the user provides one.

agenttrace --latest
agenttrace --latest -f json
agenttrace path/to/session-or-export.json
agenttrace --overview -d path/to/session-dir

Step 4: Compare Attempts When Semantics Matter

Token and latency metrics can look healthy even when an agent confidently takes the wrong implementation path. When the risk is semantic drift, pair the trace audit with a diff against a previous or known-good attempt.

Look for:

• changed files or commands that diverge from the intended task
• missing tests or verification steps compared with the reference attempt