Parse AI agent audit logs to identify bottlenecks, errors, and anomalies.
npx skills add https://github.com/alangeb/tau --skill tau-audit
Please help me install this Agent Skill. Skill: tau_audit Source: https://github.com/alangeb/tau/tree/main/src/skills/tau_audit Command: npx skills add https://github.com/alangeb/tau --skill tau-audit
This Skill solves the difficulty of debugging complex AI agent sessions by providing a structured, automated way to parse and analyze audit logs for errors, loops, and tool usage patterns.
Use the tau_audit skill to analyze the session log file located at .audit and generate a comprehensive health report.
High-intent search queries and answers about installing and using this skill.
To debug AI agent loops, parse structured audit logs to detect loop occurrences, pinpoint failing tool calls, and identify the root cause of repeated execution errors.
Analyzing AI agent performance bottlenecks involves parsing audit logs to evaluate tool success rates, measure tool chain latency, and correlate call sequences with system warnings.
Yes, you can batch process multiple structured session files to evaluate overall session health, detect behavioral anomalies, and generate comprehensive health reports.
To identify behavioral anomalies in AI agent interactions, parse audit logs to extract user-to-assistant patterns, monitor subagent nesting, and detect execution deviations.
Yes, AI agent log analysis evaluates context compression efficiency by correlating structured audit log data with assistant response quality and system warnings during execution.
AI agent sessions break down when tool call sequences trigger execution errors or loops; root cause analysis correlates these sequences with response quality and system warnings to identify failures.
alangebAnalyze session audit logs to detect execution loops and adjust strategies.
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vrcmsGenerate behavioral specifications and run stress tests to measure agent adherence.
