Data Analyzer
The Data Analyzer skill queries and analyzes data from a live Infrahub instance using the Infrahub MCP server. It answers operational questions that span multiple node types — correlating data, detecting drift, tracing service impact, auditing data quality — without requiring the user to write GraphQL queries manually.
Requirements
The Data Analyzer skill requires the Infrahub MCP server to be configured and connected to your AI tool. See the MCP server docs for setup instructions.
Without MCP, the AI can still query Infrahub data by constructing GraphQL API calls directly. The interaction is more verbose and less intuitive, but functional.
When to use
- Answering ad-hoc operational questions about live infrastructure data
- Cross-referencing two or more node types to find relationships or gaps
- Investigating the blast radius of a change before executing it
- Auditing data quality (missing fields, stale records, naming violations)
- Exploring schema structure and data before writing a generator or check
- Producing one-time or on-demand reports
Types of analysis
| Analysis type | Example question |
|---|---|
| Compliance | "Are all devices following the naming convention <site>-<role>-<number>?" |
| Service impact | "Which services are hosted on devices in Rack A-03?" |
| Maintenance windows | "Which devices are in a maintenance window, and what BGP sessions depend on them?" |
| Drift detection | "Which realized devices differ from their topology design object?" |
| Capacity | "Which racks are over 80% utilized?" |
| Inventory gaps | "Which devices have no platform or OS version recorded?" |
| Change impact | "What BGP sessions, services, and IP allocations depend on this prefix?" |
How it works
The skill uses MCP tools (infrahub_query, infrahub_list_schema, infrahub_get) to fetch data from the running instance. For multi-step analysis, queries run sequentially or in combination — data is joined in the AI's context, not in the database. The skill reports findings with counts, lists of affected objects, and suggested next steps.
Data Analyzer vs. Check Manager vs. Transform Manager
| Use Data Analyzer when... | Use Check Manager when... | Use Transform Manager when... |
|---|---|---|
| Answering a one-time question | Enforcing a rule in the pipeline | Producing a repeatable scheduled report |
| Interactive, exploratory analysis | Blocking changes that violate policy | Generating artifacts (configs, exports) |
| No pipeline integration needed | Automated enforcement required | Output needs to be stored as a versioned artifact |