volcengine-observability-cls

What This Skill Does

The volcengine-observability-cls skill acts as a bridge between the OpenClaw agent and your Volcengine Cloud Log Service (CLS) infrastructure. It is designed to automate the heavy lifting of log management, enabling engineers to perform structured log investigations, execute complex queries, and receive intelligent summaries of system health without manually sifting through raw log streams. The skill parses log outputs to identify patterns, error spikes, and performance anomalies, providing actionable insights directly within your workflow.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal:

clawhub install openclaw/skills/skills/cinience/volcengine-observability-cls

Ensure that your environment variables are configured with the necessary Volcengine API credentials (Access Key ID and Secret Access Key) to permit the agent to read your log topics.

Use Cases

This skill is ideal for incident responders and DevOps engineers. Common use cases include:

• Root Cause Analysis: Quickly drilling down into logs during an outage to isolate failing service instances.
• Error Rate Monitoring: Tracking specific error codes across a cluster to determine if a recent deployment has introduced regressions.
• Log Aggregation: Aggregating thousands of individual request logs to calculate average latency or throughput during peak traffic windows.
• Security Auditing: Searching for unauthorized access attempts or unusual patterns in access logs.

Example Prompts

1. "Analyze the error logs for the 'user-auth-service' over the last 15 minutes and identify the top 3 exception types."
2. "Show me a summary of latency anomalies for the billing service, grouped by region, using data from the past hour."
3. "Search for all 5xx errors related to the database connection pool in the 'prod-logs' topic and suggest potential remediation steps."

Tips & Limitations

• Precision is Key: Always specify the time range clearly. Queries without explicit time bounds may return excessive data, leading to higher costs or performance timeouts.
• Structure Your Queries: Leverage the built-in filtering and aggregation syntax of CLS to reduce the amount of data transferred back to the agent.
• Data Privacy: Be mindful that this skill accesses your log data. Ensure your log topics are properly configured to mask sensitive information (PII) before analysis.
• Limits: The agent has a character limit for responses. For very large datasets, use the tool to aggregate first (e.g., count() by ...) rather than fetching raw logs directly.