behavior-analyzer

Name: behavior-analyzer
Availability: InStock
Author: ybbms

Analyze OpenClaw agent session logs to compute health scores and detect anomalies.

4|Updated Apr 3, 2026

Search Tags:#session #behavior #analyze #anomalies #trend-analysis #health-score #openclaw

Ready to run

npx skills add https://github.com/ybbms777/openclaw-claude-code-integration --skill behavior-analyzer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: behavior-analyzer
Source: https://github.com/ybbms777/openclaw-claude-code-integration/tree/main/skills/behavior-analyzer
Command: npx skills add https://github.com/ybbms777/openclaw-claude-code-integration --skill behavior-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

OpenClaw agents generate complex behavior data; this skill analyzes multiple data sources to identify anomalies and compute a session health score to guide governance and risk management.

Core Features & Use Cases

Real-time integration of self-eval, cache-monitor, evolve, and yolo_classifier data to assess session health.
Trend analysis and automated recommendations for policy updates and safer automation.
Historical persistence of session metrics for auditing and improvement.

Quick Start

Run the behavior analyzer on a given session to generate a health score and recommended actions.

Frequently Asked Questions about behavior-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze agent session behavior and compute a health score?▼

To analyze agent session behavior and compute a health score, run the analyzer against workspace logs like .self-eval-reflections.jsonl and .cache-monitor.json. It processes data from self-eval, cache-monitor, evolve, and yolo_classifier sources to generate per-session scores and actionable recommendations.

What data sources do I need for OpenClaw session anomaly detection?▼

OpenClaw session anomaly detection requires access to workspace data files including .self-eval-reflections.jsonl, .evolve-rule-applications.jsonl, .cache-monitor.json, and .permission-decisions.jsonl. These sources provide the self-eval, cache, and classifier data needed to detect anomalies.

Can I integrate session behavior analysis into an automated Python workflow?▼

You can integrate session behavior analysis into an automated workflow using the exposed Python API and CLI. This allows you to programmatically compute session health scores and apply trend analysis within your existing automation pipelines.

How does trend analysis for agent sessions improve governance and risk management?▼

Trend analysis improves governance and risk management by tracking session health scores over time. It identifies persistent anomalies and provides automated recommendations for policy updates, enabling safer automation and historical persistence for auditing.

What is the best way to monitor OpenClaw agent sessions for real-time anomalies?▼

The best way to monitor OpenClaw agent sessions for real-time anomalies is to analyze multiple data sources simultaneously. Integrating self-eval, cache-monitor, and evolve data computes a comprehensive health score and detects behavioral deviations.

Are there limitations when analyzing yolo_classifier data for session health?▼

A limitation when analyzing yolo_classifier data is that the session health score depends entirely on the availability and quality of workspace logs. Without properly populated .permission-decisions.jsonl files, anomaly detection and trend analysis will be incomplete.