A local-first log diagnostic tool that provides automated error detection, statistical analysis, and visual anomaly identification without external cloud dependencies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install system-log-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 system-log-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 Log Analyzer?
Log Analyzer is a specialized diagnostic tool within the Openclaw Skills ecosystem designed for deep inspection of system and application logs. It operates entirely locally, ensuring data privacy and security while providing comprehensive insights into log patterns, error frequencies, and distribution trends. By leveraging Python's robust data science stack, it transforms raw text logs into actionable visual intelligence.
The tool is particularly effective for developers and sysadmins who need to identify recurring issues, monitor error trends, or investigate performance anomalies. It supports various standard log formats, including timestamps, log levels, and specific error types, making it a versatile asset for debugging and system maintenance within the Openclaw Skills framework.
Log Analyzer Use Cases
- Investigating application errors and critical failures in local log files.
- Analyzing log volume and line counts to manage storage and performance.
- Detecting duplicate log entries to identify redundant operations or infinite loops.
- Visualizing error trends over time to pinpoint when specific system anomalies occur.
- Automating log statistical summaries during development or post-mortem analysis.
How Log Analyzer Works
- The skill scans the designated log directory for files matching specified patterns like .log, .txt, or .csv.
- The LogParser module utilizes pattern matching to extract timestamps, log levels (INFO, ERROR, etc.), and error categories.
- LogAnalyzer processes the parsed data to calculate statistics, identify high-frequency duplicates, and detect statistical anomalies based on standard deviation.
- The ResultDisplay module renders the findings into an interactive Web UI powered by Streamlit.
- Users can filter results by file type, count, or display options to drill down into specific technical issues.
Log Analyzer Setup
To install and run the Log Analyzer from the Openclaw Skills collection, ensure you have Python 3.7+ installed, then follow these steps:
# Navigate to the skill directory
cd ~/.openclaw/workspace/skills/log-analyzer
# Install required Python dependencies
pip install streamlit pandas plotly
# Launch the analyzer via Streamlit
streamlit run log-analyzer.py --server.port 8506
Alternatively, use the provided startup script:
./start-log-analyzer.sh
Log Analyzer Data Schema & Taxonomy
The skill organizes its analysis into a structured hierarchy to ensure clarity in diagnostic reporting:
| Component | Responsibility | Output Data |
|---|---|---|
| LogParser | Line-by-line parsing | Timestamps, Levels (DEBUG/ERROR), IP Addresses, Attack Types |
| LogAnalyzer | Data processing | Global stats (file size, lines), Duplication rates, Error trends |
| ResultDisplay | Visualization | Plotly charts, Top Error lists, Anomaly markers |
Log Analyzer Advanced Features
- Multi-format support for diverse log standards including SQL injection and XSS attack pattern recognition.
- Automated anomaly detection using statistical standard deviation for time-based events.
- High-performance hashing for rapid identification of identical log entries across massive datasets.
- Configurable web-based dashboard for real-time adjustments of analysis depth and visual output filters.
- Seamless integration with the Openclaw Skills environment for automated activation via natural language triggers.
SKILL.md
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