lmiranda
a74a048898
Add validation hooks, best practices skill, and new commands to enforce NetBox data quality standards: Hooks: - SessionStart: Test NetBox connectivity, report data quality issues - PreToolUse: Validate VM/device parameters before create/update New Commands: - /cmdb-audit: Data quality analysis (vms, devices, naming, roles) - /cmdb-register: Register current machine with running applications - /cmdb-sync: Sync machine state with NetBox, detect drift Best Practices Skill: - Dependency order (regions -> sites -> devices -> VMs) - Site/tenant/platform assignment requirements - Naming conventions enforcement - Role consolidation guidance Updated agent with validation requirements, dependency order checks, naming convention warnings, and duplicate prevention. Marketplace: 5.0.0 -> 5.1.0 Plugin: 1.0.0 -> 1.1.0 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
196 lines
5.0 KiB
Markdown
196 lines
5.0 KiB
Markdown
---
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description: Audit NetBox data quality and identify consistency issues
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---
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# CMDB Data Quality Audit
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Analyze NetBox data for quality issues and best practice violations.
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## Usage
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```
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/cmdb-audit [scope]
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```
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**Scopes:**
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- `all` (default) - Full audit across all categories
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- `vms` - Virtual machines only
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- `devices` - Physical devices only
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- `naming` - Naming convention analysis
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- `roles` - Role fragmentation analysis
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## Instructions
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You are a data quality auditor for NetBox. Your job is to identify consistency issues and best practice violations.
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**IMPORTANT:** Load the `netbox-patterns` skill for best practice reference.
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### Phase 1: Data Collection
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Run these MCP tool calls to gather data for analysis:
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```
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1. virt_list_vms (no filters - get all)
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2. dcim_list_devices (no filters - get all)
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3. virt_list_clusters (no filters)
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4. dcim_list_sites
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5. tenancy_list_tenants
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6. dcim_list_device_roles
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7. dcim_list_platforms
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```
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Store the results for analysis.
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### Phase 2: Quality Checks
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Analyze collected data for these issues by severity:
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#### CRITICAL Issues (must fix immediately)
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| Check | Detection |
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|-------|-----------|
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| VMs without cluster | `cluster` field is null AND `site` field is null |
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| Devices without site | `site` field is null |
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| Active devices without primary IP | `status=active` AND `primary_ip4` is null AND `primary_ip6` is null |
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#### HIGH Issues (should fix soon)
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| Check | Detection |
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|-------|-----------|
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| VMs without site | VM has no site (neither direct nor via cluster.site) |
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| VMs without tenant | `tenant` field is null |
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| Devices without platform | `platform` field is null |
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| Clusters not scoped to site | `site` field is null on cluster |
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| VMs without role | `role` field is null |
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#### MEDIUM Issues (plan to address)
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| Check | Detection |
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|-------|-----------|
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| Inconsistent naming | Names don't match patterns: devices=`{role}-{site}-{num}`, VMs=`{env}-{app}-{num}` |
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| Role fragmentation | More than 10 device roles with <3 assignments each |
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| Missing tags on production | Active resources without any tags |
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| Mixed naming separators | Some names use `_`, others use `-` |
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#### LOW Issues (informational)
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| Check | Detection |
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|-------|-----------|
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| Docker containers as VMs | Cluster type is "Docker Compose" - document this modeling choice |
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| VMs without description | `description` field is empty |
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| Sites without physical address | `physical_address` is empty |
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| Devices without serial | `serial` field is empty |
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### Phase 3: Naming Convention Analysis
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For naming scope, analyze patterns:
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1. **Extract naming patterns** from existing objects
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2. **Identify dominant patterns** (most common conventions)
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3. **Flag outliers** that don't match dominant patterns
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4. **Suggest standardization** based on best practices
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**Expected Patterns:**
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- Devices: `{role}-{location}-{number}` (e.g., `web-dc1-01`)
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- VMs: `{prefix}_{service}` or `{env}-{app}-{number}` (e.g., `prod-api-01`)
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- Clusters: `{site}-{type}` (e.g., `home-docker`)
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### Phase 4: Role Analysis
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For roles scope, analyze fragmentation:
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1. **List all device roles** with assignment counts
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2. **Identify single-use roles** (only 1 device/VM)
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3. **Identify similar roles** that could be consolidated
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4. **Suggest consolidation** based on patterns
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**Red Flags:**
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- More than 15 highly specific roles
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- Roles with technology in name (use platform instead)
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- Roles that duplicate functionality
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### Phase 5: Report Generation
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Present findings in this structure:
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```markdown
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## CMDB Data Quality Audit Report
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**Generated:** [timestamp]
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**Scope:** [scope parameter]
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### Summary
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| Metric | Count |
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|--------|-------|
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| Total VMs | X |
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| Total Devices | Y |
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| Total Clusters | Z |
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| **Total Issues** | **N** |
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| Severity | Count |
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|----------|-------|
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| Critical | A |
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| High | B |
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| Medium | C |
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| Low | D |
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### Critical Issues
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[List each with specific object names and IDs]
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**Example:**
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- VM `HotServ` (ID: 1) - No cluster or site assignment
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- Device `server-01` (ID: 5) - No site assignment
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### High Issues
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[List each with specific object names]
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### Medium Issues
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[Grouped by category with counts]
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### Recommendations
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1. **[Most impactful fix]** - affects N objects
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2. **[Second priority]** - affects M objects
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...
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### Quick Fixes
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Commands to fix common issues:
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```
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# Assign site to VM
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virt_update_vm id=X site=Y
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# Assign platform to device
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dcim_update_device id=X platform=Y
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```
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### Next Steps
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- Run `/cmdb-register` to properly register new machines
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- Use `/cmdb-sync` to update existing registrations
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- Consider bulk updates via NetBox web UI for >10 items
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```
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## Scope-Specific Instructions
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### For `vms` scope:
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Focus only on Virtual Machine checks. Skip device and role analysis.
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### For `devices` scope:
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Focus only on Device checks. Skip VM and cluster analysis.
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### For `naming` scope:
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Focus on naming convention analysis across all objects. Generate detailed pattern report.
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### For `roles` scope:
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Focus on role fragmentation analysis. Generate consolidation recommendations.
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## User Request
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$ARGUMENTS
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