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Skillsinfrahub-analyzing-data

infrahub-analyzing-data

Data & analytics

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Updated 10-16 03:11

About this skill

Overview

Expert guidance for interactive data analysis against a live Infrahub instance. This skill uses the Infrahub MCP server to query, correlate, and reason over infrastructure data on demand — answering operational questions that span multiple node types and relationships.

Use this skill for any question of the form "what does Infrahub currently know about X, and how does it relate to Y?"

Typical question patterns:

  • Compliance — "Are all devices following the naming convention?"
  • Service impact — "Which services are hosted on devices in this rack?"
  • Maintenance windows — "Which devices are currently in a maintenance window, and what depends on them?"
  • Drift detection — "Which realized devices differ from their topology design?"
  • Capacity — "Which racks are over 80% full?"
  • Change impact — "What BGP sessions, services, and IPs depend on this prefix?"
  • Inventory gaps — "Which devices have no platform or OS version recorded?"

For automated, pipeline-enforced checks that block proposed changes, see ../infrahub-managing-checks/SKILL.md. For repeatable scheduled reports exported as artifacts, see ../infrahub-managing-transforms/SKILL.md.

Project Context

This skill runs in a forked subagent context and has no visibility into the parent conversation. The user's question MUST be passed via arguments.

  • If invoked with arguments (e.g., /infrahub:analyzing-data Which devices have no platform assigned?), treat the arguments as the question to answer.

  • If invoked with no arguments, do not guess, do not use any example question from this file, and do not proceed. Return immediately with:

    Error: No question was passed to infrahub-analyzing-data. This skill runs in a forked context and requires the user's question as args. Re-invoke with the question, e.g. Skill(skill="infrahub-analyzing-data", args="<the user's question>").

When to Use

  • Answering operational questions interactively via natural language
  • 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 across the inventory
  • Producing one-time or on-demand reports for stakeholders
  • Exploring schema structure and data before writing a generator or check

How It Works

The Infrahub MCP server exposes tools that let Claude query Infrahub data directly. The typical workflow:

  1. Query — use MCP tools to fetch current state from Infrahub
  2. Correlate — join, diff, or filter the data against a policy or second dataset
  3. Reason — identify gaps, anomalies, or relationships
  4. Report — surface findings with context and remediation hints

Rule Categories

Priority Category Prefix Description
CRITICAL MCP Tools mcp- Available Infrahub MCP tools, invocation patterns, response structure
CRITICAL Query Patterns query- GraphQL structures for fetching, filtering, and traversing relationships
HIGH Correlation correlation- Joining, diffing, and reasoning over data from multiple queries
HIGH Reporting Output reporting- Presenting findings: summaries, tables, per-object detail, remediation hints
MEDIUM Approach Selection approach- When to use MCP analysis vs InfrahubCheck vs Transform

MCP Server Basics

When the Infrahub MCP server (v1.1.7) is connected, Claude can call these tools.

Read:

  • mcp__infrahub__get_nodes — List nodes of a kind with filtering/pagination (preferred typed read)
  • mcp__infrahub__search_nodes — Find nodes of a kind by partial substring
  • mcp__infrahub__get_schema — Discover schema kinds and their filters
  • mcp__infrahub__query_graphql — Execute a read-only GraphQL query
  • mcp__infrahub__get_session_info — Report the active session branch and instance address

Write (branch-isolated — land on an auto-created mcp/session-* branch, never the default branch):

  • mcp__infrahub__node_upsert — Create or update an object
  • mcp__infrahub__node_delete — Delete an object
  • mcp__infrahub__mutate_graphql — Run a GraphQL mutation for complex writes
  • mcp__infrahub__propose_changes — Open a Proposed Change for human review
  • mcp__infrahub__reset_session_branch — Reset or switch the active session branch

Full per-tool signatures (parameters, examples, response shapes) and the branch model are in rules/mcp-tools.md — read that before invoking any of these.

# Example: find all devices in an active
# maintenance window
query MaintenanceDevices {
  MaintenanceWindow(status__value: "active") {
    edges {
      node {
        name { value }
        start_time { value }
        end_time { value }
        devices {
          edges {
            node {
              name { value }
              role { value }
              site {
                node { name { value } }
              }
            }
          }
        }
      }
    }
  }
}

Typical Analysis Workflow

1. Understand the question
   → "Which services depend on devices currently
      in a maintenance window?"

2. Identify the node types involved
   → MaintenanceWindow, DcimDevice, Service
     (or equivalent in your schema)

3. Query current state
   → mcp__infrahub__get_nodes per kind (typed), or
     mcp__infrahub__query_graphql — one query
     per node type, or combined

4. Correlate the data
   → Join across node types, filter, count, diff

5. Report findings
   → Summarize with counts, list affected objects,
     suggest next steps

Supporting References