What problem does it solve?
Working with OpenObserve programmatically is error-prone: there is no official CLI, timestamps are in microseconds, dashboard updates require hash-based optimistic concurrency, and hand-written SQL panels silently re-aggregate data. This Skill gives an AI agent the exact curl commands, panel JSON schema, and known pitfalls needed to operate any OpenObserve Cloud or self-hosted instance correctly.
Core Features & Use Cases
- Search and SQL queries: Run DataFusion SQL against logs, metrics, and traces with correct microsecond time ranges, pagination, histograms, and full-text functions like match_all and str_match.
- Dashboard automation: Create, read, update, and delete dashboards and individual panels using the v8 panel JSON schema, with hash-aware PUT requests that avoid 409 conflicts.
- Ingestion and stream management: Ingest data via JSON, NDJSON, Elasticsearch bulk, OTLP, Loki, or Prometheus remote-write, and manage stream schemas, settings, folders, and alerts.
- Use Case: Ask the agent to build a cost-tracking dashboard: it fetches an existing dashboard as a template, adds a metric panel with a SUM query, sets the currency unit, and PUTs the unwrapped v8 body back with the current hash.
Quick Start
Ask the agent to query the top hosts by message count from your OpenObserve stream over the last 24 hours using your instance URL, organization, and credentials.