An automated diagnostic tool for analyzing the startup and shutdown performance metrics of SS4 vehicle head units.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install boot-kpi-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install boot-kpi-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Boot KPI Analyzer?
The Boot KPI Analyzer is a specialized technical tool designed for performance engineers and developers working on SS4 vehicle head unit systems. This skill automates the parsing and analysis of complex time-series data associated with system boot and shutdown sequences. By utilizing Openclaw Skills, developers can quickly identify which system services are causing delays, monitor the health of the Android VM (qcrosvm), and ensure that the total reboot cycle meets strict performance targets.
The tool bridges the gap between raw BPF-traced logs and actionable insights, providing structured summaries that highlight slow services, dependency issues, and failed activation states. It is essential for maintaining high-performance standards in automotive software environments where startup speed is critical for user experience.
Boot KPI Analyzer Use Cases
- Identifying system services that take longer than 100ms to activate during boot.
- Diagnosing slow shutdown issues by tracking unit state transitions to the inactive state.
- Monitoring the startup and shutdown performance of the Android VM (qcrosvm.service).
- Verifying that total reboot duration stays within the optimized target of under 40 seconds.
- Analyzing service dependency chains to find root causes for frequent service restarts.
How Boot KPI Analyzer Works
- The skill identifies the latest or specified data directories containing boot and shutdown logs from the SS4 system.
- It parses raw log files including boot_kpi.log and shutdown-monitor-units.log using a specialized Python analysis script.
- The analyzer calculates time differences between service states (e.g., from activating to active) to determine latency.
- It extracts high-level test results and firmware version metadata from JSON-formatted reports.
- The skill synthesizes this data into a comprehensive performance summary or allows for deep-dive queries via a localized web API using curl commands.
Boot KPI Analyzer Setup
To initialize the analyzer within your Openclaw Skills environment, ensure the analysis scripts are mapped to the correct paths. Use the following commands to interact with the performance data:
# List all available datasets for analysis
python3 /home/lixiang/.openclaw/skills/boot-kpi-analyzer/scripts/analyze.py list
# Perform an automated analysis on the most recent boot cycle
python3 /home/lixiang/.openclaw/skills/boot-kpi-analyzer/scripts/analyze.py boot --latest
# Query the internal performance monitoring API
curl -s http://10.122.86.46:9999/api/latest
Boot KPI Analyzer Data Schema & Taxonomy
The skill organizes performance data into two primary categories with the following structures:
Boot KPI Data
| File Name | Data Type | Description |
|---|---|---|
boot_kpi.log |
Time-series | Raw events: service name, state transition, and millisecond offset. |
boot_kpi_summary.txt |
Text Report | Summarized analysis of system services and qcrosvm performance. |
unit_kpi.log |
Dependency Log | Detailed modemanager dependency and qcrosvm service logs. |
Shutdown KPI Data
| File Name | Data Type | Description |
|---|---|---|
report.json |
JSON Metadata | Key fields: reboot_duration, test_result, build_id, and error_message. |
shutdown-monitor-units.log |
BPF Trace | High-precision logs with nanosecond BPF timestamps for unit shutdowns. |
Boot KPI Analyzer Advanced Features
- BPF-based high-precision timestamp tracking for nanosecond-level shutdown analysis.
- Automated dependency analysis for the qcrosvm.service and its related Android VM components.
- Private network API integration using curl to bypass browser restrictions for internal metrics servers.
- Comparative analysis support to identify performance regressions between different firmware build IDs.
- Detection of 'lastlog' flags to identify and categorize abnormal system reboots.
SKILL.md
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