InfluxDB

What This Skill Does

The InfluxDB skill provides OpenClaw with native capabilities to interface with time-series databases. It handles structured data ingestion, efficient query formulation, and complex schema management. The agent can construct properly formatted Line Protocol strings, perform downsampling tasks, and debug high-cardinality issues. It supports both legacy 1.x architectures and modern 2.x paradigms, ensuring data is stored according to best practices for retention and indexing.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/ivangdavila/influxdb

Use Cases

• IoT Telemetry: Storing sensor data (temperature, pressure, vibration) with high-frequency resolution.
• Application Monitoring: Tracking microservice latency, CPU usage, and request counts across distributed clusters.
• Business Intelligence: Aggregating historical financial transaction logs to visualize long-term performance trends.
• Infrastructure Optimization: Downsampling raw logs into summarized buckets to save storage costs while maintaining dashboard performance.

Example Prompts

1. "Check the current database cardinality for the 'web_requests' bucket and identify any tags causing performance degradation."
2. "Write a Flux query to calculate the 95th percentile latency for the 'api_server' measurement over the last 24 hours, grouped by region."
3. "Help me design an InfluxDB schema for a smart home sensor network; I need to decide which values should be tags versus fields to ensure optimal query performance."

Tips & Limitations

• The Cardinality Trap: Never use high-cardinality data like unique user IDs or individual request IDs as tags. This creates an index explosion that will bring your database to a halt. Always use fields for high-variety data.
• Tag vs Field: If you filter or group by it, it's a tag. If you calculate an average, count, or sum, it's a field.
• Timestamps: Always specify your precision. If you are sending data from a system producing seconds, explicitly set your write precision to 's' to prevent your data from appearing to be from the year 2000.
• Downsampling: Do not keep raw data forever. Use the agent to set up retention policies or automated tasks to downsample data into aggregate buckets, which keeps query response times fast as your data grows over time.