CloudOps Runbooks Inventory - Advanced Guide¶
Target Audience: Technical users, SREs, Cloud Architects, FinOps practitioners, Security Engineers
📋 Command Categories - Detailed Reference¶
1️⃣ Discovery Commands¶
🔍 Multi-account resource discovery (10 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `resource-explorer` | Discover 88 AWS resource types | CENTRALISED_OPS | CSV/JSON | Universal discovery | | `discover-ec2` | EC2 instance discovery | CENTRALISED_OPS | CSV | Compute inventory | | `discover-rds` | RDS database discovery | CENTRALISED_OPS | CSV | Database inventory | | `discover-lambda` | Lambda function discovery | CENTRALISED_OPS | CSV | Serverless inventory | | `discover-workspaces` | WorkSpaces discovery | CENTRALISED_OPS | CSV | VDI cost analysis | | `discover-snapshots` | EBS snapshot discovery | CENTRALISED_OPS | CSV | Storage optimization | | `list-s3-buckets` | S3 bucket enumeration | CENTRALISED_OPS | CSV | Storage inventory | | `list-elbs` | Load balancer discovery | CENTRALISED_OPS | CSV | Network inventory | | `list-vpcs` | VPC discovery | CENTRALISED_OPS | CSV | Network topology | | `resource-types` | List 88 supported types | Any | Table | Reference documentation | **Integration Example**: **CLI Example**:2️⃣ Organizations Commands¶
🏢 Multi-account organization mapping (3 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `draw-org` | Visualize AWS organization structure | CENTRALISED_OPS | JSON/Markdown | Architecture documentation | | `enrich-accounts` | Add account metadata to inventory | CENTRALISED_OPS | CSV | Account context enrichment | | `list-accounts` | List organization accounts | CENTRALISED_OPS | Table/CSV | Account discovery | **Integration Example**: **CLI Example**:3️⃣ Cost Enrichment Commands¶
💰 Cost data integration (3 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `enrich-costs` | Add cost data to inventory | CENTRALISED_OPS | CSV | Cost visibility | | `validate-costs` | Cross-validate cost accuracy | CENTRALISED_OPS | JSON | Quality assurance | | `historical-costs` | 6-month cost analysis | CENTRALISED_OPS | CSV/JSON | Trend analysis | **Integration Example**:# Taskfile integration
task -t Taskfile.inventory.yaml enrich-costs
# Notebook integration
import pandas as pd
subprocess.run([
'runbooks', 'inventory', 'enrich-costs',
'--input', '/tmp/resources.csv',
'--output', '/tmp/resources-with-costs.csv'
], check=True)
df = pd.read_csv('/tmp/resources-with-costs.csv')
# Add cost data to EC2 inventory
runbooks inventory enrich-costs \
--input ec2-inventory.csv \
--output ec2-with-costs.csv
# Validate cost accuracy (≥99.5% target)
runbooks inventory validate-costs \
--input ec2-with-costs.csv \
--output validation-results.json
# Historical cost analysis (6 months)
runbooks inventory historical-costs \
--output cost-analysis.csv
4️⃣ Activity Enrichment Commands¶
📊 Resource activity signals (2 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `enrich-activity` | Add activity signals (E1-E7, W1-W6, S1-S7, L1-L6) | CENTRALISED_OPS | CSV | Usage patterns | | `analyze-activity` | Activity pattern analysis | CENTRALISED_OPS | JSON | Decommission candidates | **Integration Example**: **CLI Example**: **Activity Signals Reference**: - **EC2 (E1-E7)**: CloudTrail API calls, CloudWatch metrics, EBS activity, Network I/O, Instance state, CPU utilization, Age analysis - **WorkSpaces (W1-W6)**: Connection frequency, User activity, Data transfer, Session duration, Login patterns, Usage hours - **Snapshots (S1-S7)**: AMI association, Volume existence, Age, Size, Encryption, Tags, Lifecycle status - **Lambda (L1-L6)**: Invocation count, Error rate, Duration, Memory usage, Concurrency, Last invoked5️⃣ Scoring Commands¶
🎯 Decommission prioritization (2 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `score-decommission` | Calculate decommission scores (0-100) | CENTRALISED_OPS | CSV | Savings prioritization | | `tier-resources` | Classify by decommission likelihood | CENTRALISED_OPS | JSON | Risk stratification | **Integration Example**: **CLI Example**: **Scoring Logic**: - **90-100**: High confidence decommission (idle, stopped, old) - **70-89**: Moderate confidence (low usage, redundant) - **50-69**: Review recommended (underutilized) - **0-49**: Keep running (active, critical)6️⃣ Validation Commands¶
✅ Quality assurance & cross-validation (3 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `validate-mcp` | MCP server cross-validation | CENTRALISED_OPS | JSON | Accuracy verification | | `validate-costs` | Cost data accuracy check | CENTRALISED_OPS | JSON | Financial validation | | `validate-discovery` | Discovery completeness check | CENTRALISED_OPS | JSON | Coverage verification | **Integration Example**: **CLI Example**: **Validation Standards**: - **MCP Accuracy**: ≥99.5% required for production readiness - **Cost Variance**: <0.5% deviation acceptable - **Discovery Coverage**: 100% of accessible accounts7️⃣ Workflow Commands¶
⚡ Automated pipelines (3 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `workflow-multi-account` | Full 5-layer pipeline (all accounts) | CENTRALISED_OPS | CSV | Enterprise automation | | `workflow-single-account` | 5-layer pipeline (single account) | Any | CSV | Focused analysis | | `workflow-custom` | Custom pipeline configuration | CENTRALISED_OPS | CSV | Specialized workflows | **Integration Example**: **CLI Example**: **5-Layer Pipeline Architecture**: 1. **Discovery**: Resource enumeration (88 types) 2. **Organizations**: Account metadata enrichment 3. **Costs**: Financial data integration 4. **Activity**: Usage signals (E1-E7, W1-W6, S1-S7, L1-L6) 5. **Scoring**: Decommission prioritization (0-100)8️⃣ Export Commands¶
📤 Multi-format data export (4 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `export-csv` | CSV export with filtering | Any | CSV | Excel analysis | | `export-json` | JSON export for APIs | Any | JSON | System integration | | `export-markdown` | Markdown tables | Any | Markdown | Documentation | | `export-excel` | Excel workbook (multi-sheet) | Any | XLSX | Executive reporting | **Integration Example**:# Taskfile integration
task -t Taskfile.inventory.yaml export-excel
# Notebook integration
subprocess.run([
'runbooks', 'inventory', 'export-excel',
'--input', '/tmp/inventory.csv',
'--output', '/tmp/report.xlsx'
], check=True)
# Export to Excel (multi-sheet workbook)
runbooks inventory export-excel \
--input ec2-scored.csv \
--output ec2-report.xlsx
# Export to JSON for API integration
runbooks inventory export-json \
--input ec2-scored.csv \
--output ec2-data.json
# Export to Markdown for documentation
runbooks inventory export-markdown \
--input ec2-scored.csv \
--output ec2-table.md
9️⃣ Utility Commands¶
🔧 Helper & reference commands (5 commands)
| Command | Purpose | Profile | Output Format | Use Case | |---------|---------|---------|---------------|----------| | `resource-types` | List 88 supported resource types | Any | Table | Reference | | `list-profiles` | AWS profile enumeration | Any | Table | Configuration | | `test-connection` | AWS connectivity check | Any | JSON | Troubleshooting | | `version` | Show CLI version | Any | Text | Version verification | | `help` | Command documentation | Any | Text | Quick reference | **CLI Example**:👥 User Persona Mapping¶
Persona 1: 💼 CxO / Executive¶
Goals: Cost visibility, decommission opportunities, compliance status, ROI measurement
Recommended Commands:
# Executive dashboard: Full pipeline with decommission scoring
runbooks inventory workflow-multi-account \
--output executive-dashboard.csv
# Cost validation for financial accuracy
runbooks inventory validate-costs \
--input executive-dashboard.csv \
--output cost-validation.json
# Excel export for board presentations
runbooks inventory export-excel \
--input executive-dashboard.csv \
--output executive-report.xlsx
Key Metrics: - Total resource count across organization - Monthly cost by account/resource type - Decommission savings opportunities (high-score resources) - Cost accuracy validation (≥99.5%)
Typical Questions Answered: - "What are our top cost drivers?" - "What savings can we achieve from idle resources?" - "What's our cloud spend by business unit?"
Persona 2: 💰 FinOps Practitioner¶
Goals: Cost optimization, rightsizing, orphaned resource cleanup, budget tracking
Recommended Commands:
# Cost enrichment for financial analysis
runbooks inventory enrich-costs \
--input resources.csv \
--output resources-with-costs.csv
# Decommission scoring for cleanup prioritization
runbooks inventory score-decommission \
--input resources-with-costs.csv \
--output scored-resources.csv
# Historical cost trends (6 months)
runbooks inventory historical-costs \
--output cost-trends.csv
# Cost validation for accuracy
runbooks inventory validate-costs \
--input scored-resources.csv \
--output validation.json
Key Metrics: - Resource cost by tag/account/type - Decommission candidates with cost impact - Cost variance vs. budget - Orphaned resource identification
Typical Questions Answered: - "Which resources have highest decommission scores?" - "What's the cost of idle/stopped instances?" - "Are our cost tags accurate?"
Persona 3: 🔧 SRE / Operations¶
Goals: Resource discovery, inventory accuracy, operational efficiency, automation
Recommended Commands:
# Multi-account resource discovery
runbooks inventory resource-explorer \
--resource-type ec2 \
--output ec2-inventory.csv
# MCP validation for accuracy
runbooks inventory validate-mcp \
--input ec2-inventory.csv \
--output validation.json
# Single-account workflow for focused analysis
runbooks inventory workflow-single-account \
--profile PRODUCTION \
--output production-inventory.csv
# Activity enrichment for usage patterns
runbooks inventory enrich-activity \
--input ec2-inventory.csv \
--output ec2-activity.csv
Key Metrics: - Resource count by state (running/stopped) - Activity signals (E1-E7) for usage patterns - Discovery completeness (100% coverage) - MCP validation accuracy (≥99.5%)
Typical Questions Answered: - "What resources are running in production?" - "Which instances have low activity?" - "Is our inventory accurate?"
Persona 4: 🔒 Security Engineer¶
Goals: Compliance validation, security posture, audit trails, vulnerability management
Recommended Commands:
# Resource discovery for security audit
runbooks inventory resource-explorer \
--resource-type ec2 \
--output ec2-security-audit.csv
# Tag coverage for compliance
runbooks inventory validate-discovery \
--input ec2-security-audit.csv \
--output discovery-validation.json
# Organization structure for access review
runbooks inventory draw-org \
--output org-structure.json
# Export for security tools
runbooks inventory export-json \
--input ec2-security-audit.csv \
--output ec2-data.json
Key Metrics: - Resource inventory completeness - Tag coverage for compliance tracking - Account organization structure - Resource lifecycle status
Typical Questions Answered: - "What resources exist in production?" - "Are all resources properly tagged?" - "What's our organization structure?"
Persona 5: 🏗️ Cloud Architect¶
Goals: Architecture assessment, network topology, IaC drift, resource dependencies
Recommended Commands:
# Organization visualization
runbooks inventory draw-org \
--output org-architecture.json
# VPC discovery for network topology
runbooks inventory list-vpcs \
--output vpc-topology.csv
# Multi-account discovery for architecture review
runbooks inventory workflow-multi-account \
--output architecture-inventory.csv
# Export to markdown for documentation
runbooks inventory export-markdown \
--input architecture-inventory.csv \
--output architecture-docs.md
Key Metrics: - Organization structure (OUs, accounts) - VPC topology and networking - Resource distribution by account - Account metadata and tags
Typical Questions Answered: - "What's our AWS organization structure?" - "How are resources distributed?" - "What's our network topology?"
Persona 6: 💻 Application Developer¶
Goals: Resource discovery, dependency mapping, development resources, troubleshooting
Recommended Commands:
# Lambda function discovery
runbooks inventory discover-lambda \
--output lambda-inventory.csv
# RDS database discovery
runbooks inventory discover-rds \
--output rds-inventory.csv
# Load balancer enumeration
runbooks inventory list-elbs \
--output elb-inventory.csv
# S3 bucket discovery
runbooks inventory list-s3-buckets \
--output s3-inventory.csv
Key Metrics: - Development resource inventory - Resource dependencies (ELB → EC2 → RDS) - Resource configuration details - Resource state and availability
Typical Questions Answered: - "What Lambda functions exist?" - "Which databases are available?" - "What load balancers are configured?"
Persona 7: 📋 Compliance / Audit¶
Goals: Landing Zone validation, Control Tower compliance, drift detection, audit trails
Recommended Commands:
# Complete organization inventory for audit
runbooks inventory workflow-multi-account \
--output audit-inventory.csv
# Validation for audit evidence
runbooks inventory validate-mcp \
--input audit-inventory.csv \
--output mcp-validation.json
# Cost validation for financial audit
runbooks inventory validate-costs \
--input audit-inventory.csv \
--output cost-validation.json
# Excel export for audit documentation
runbooks inventory export-excel \
--input audit-inventory.csv \
--output audit-report.xlsx
Key Metrics: - Complete resource inventory (audit trail) - Validation accuracy (≥99.5% MCP, <0.5% cost variance) - Discovery completeness (100% accounts) - Data provenance documentation
Typical Questions Answered: - "What's our complete resource inventory?" - "Is our data accurate and auditable?" - "What's the validation evidence?"
🔗 Integration Patterns¶
Pattern 1: 📋 CLI + Taskfile Automation¶
Use Case: Scheduled inventory collection, team standardization, repeatable workflows
Implementation:
# Taskfile.inventory.yaml
version: '3'
tasks:
discover-ec2:
desc: "Discover EC2 instances"
cmds:
- runbooks inventory resource-explorer --resource-type ec2 --output data/ec2.csv
discover-rds:
desc: "Discover RDS databases"
cmds:
- runbooks inventory discover-rds --output data/rds.csv
full-workflow:
desc: "Complete 5-layer pipeline"
cmds:
- runbooks inventory workflow-multi-account --output data/complete-inventory.csv
- runbooks inventory validate-mcp --input data/complete-inventory.csv --output data/validation.json
- runbooks inventory export-excel --input data/complete-inventory.csv --output reports/inventory-report.xlsx
scheduled-inventory:
desc: "Daily scheduled inventory"
cmds:
- task: full-workflow
- echo "Inventory complete - $(date)"
Execution:
# Run single task
task -t Taskfile.inventory.yaml discover-ec2
# Run full workflow
task -t Taskfile.inventory.yaml full-workflow
# Schedule with cron
0 2 * * * cd /path/to/project && task -t Taskfile.inventory.yaml scheduled-inventory
Benefits: - ✅ Repeatable workflows across team - ✅ Scheduled automation via cron - ✅ Standardized commands - ✅ Easy onboarding for new team members
Pattern 2: 📊 CLI + Notebooks (Subprocess)¶
Use Case: Interactive analysis, visualization, data exploration, custom processing
Implementation:
# notebooks/inventory-analysis.ipynb
import subprocess
import pandas as pd
import matplotlib.pyplot as plt
# Step 1: Execute CLI command via subprocess
subprocess.run([
'runbooks', 'inventory', 'workflow-multi-account',
'--output', '/tmp/inventory.csv'
], check=True)
# Step 2: Read results in notebook
df = pd.read_csv('/tmp/inventory.csv')
# Step 3: Analysis and visualization
# Cost by resource type
cost_by_type = df.groupby('ResourceType')['MonthlyCost'].sum().sort_values(ascending=False)
cost_by_type.plot(kind='bar', title='Cost by Resource Type')
# Decommission candidates (score ≥70)
decommission_candidates = df[df['DecommissionScore'] >= 70]
potential_savings = decommission_candidates['MonthlyCost'].sum()
print(f"Potential monthly savings: ${potential_savings:,.2f}")
# Activity signal analysis
activity_columns = [col for col in df.columns if col.startswith(('E', 'W', 'S', 'L')) and col[1:].isdigit()]
low_activity = df[df[activity_columns].sum(axis=1) < 2] # <2 positive signals
print(f"Low activity resources: {len(low_activity)}")
Benefits: - ✅ Rich visualizations (charts, graphs) - ✅ Interactive data exploration - ✅ Custom analysis logic - ✅ Reproducible research
When to Use: - Exploratory data analysis - Custom visualizations - Statistical analysis - Executive presentations
Pattern 3: ✅ CLI + MCP Validation¶
Use Case: Quality assurance, accuracy verification, production readiness
Implementation:
# Step 1: Runbooks discovery
runbooks inventory resource-explorer \
--resource-type ec2 \
--output ec2-inventory.csv
# Step 2: MCP cross-validation (≥99.5% target)
runbooks inventory validate-mcp \
--input ec2-inventory.csv \
--output mcp-validation.json
# Step 3: Review validation results
cat mcp-validation.json | jq '.accuracy'
# Expected: 0.995 or higher (99.5%)
# Step 4: If accuracy met, proceed with workflow
if [ $(cat mcp-validation.json | jq '.accuracy >= 0.995') = "true" ]; then
echo "✅ MCP validation passed"
runbooks inventory workflow-multi-account --output final-inventory.csv
else
echo "❌ MCP validation failed - review discrepancies"
cat mcp-validation.json | jq '.discrepancies'
fi
Validation Standards: - MCP Accuracy: ≥99.5% required for production - Cost Variance: <0.5% deviation acceptable - Discovery Coverage: 100% of accessible accounts
Benefits: - ✅ Independent verification (3rd party MCP) - ✅ Production quality assurance - ✅ Automated accuracy checking - ✅ Audit trail for compliance
Pattern 4: ⚡ Multi-Layer Pipelines¶
Use Case: Enterprise-scale automation, complete inventory lifecycle, end-to-end workflows
Automated Pipeline (Recommended):
# Single command - full 5-layer pipeline
runbooks inventory workflow-multi-account \
--output enterprise-inventory.csv
Manual Pipeline (Understanding layers):
# Layer 1: Discovery (88 resource types)
runbooks inventory resource-explorer \
--resource-type ec2 \
--output layer1-discovery.csv
# Layer 2: Organizations (account metadata)
runbooks inventory enrich-accounts \
--input layer1-discovery.csv \
--output layer2-accounts.csv
# Layer 3: Costs (financial data)
runbooks inventory enrich-costs \
--input layer2-accounts.csv \
--output layer3-costs.csv
# Layer 4: Activity (usage signals E1-E7, W1-W6, S1-S7, L1-L6)
runbooks inventory enrich-activity \
--input layer3-costs.csv \
--output layer4-activity.csv
# Layer 5: Scoring (decommission prioritization 0-100)
runbooks inventory score-decommission \
--input layer4-activity.csv \
--output layer5-scored.csv
# Validation: MCP cross-check
runbooks inventory validate-mcp \
--input layer5-scored.csv \
--output validation.json
# Export: Excel report
runbooks inventory export-excel \
--input layer5-scored.csv \
--output enterprise-report.xlsx
Pipeline Architecture:
Discovery → Organizations → Costs → Activity → Scoring → Validation → Export
(Layer 1) (Layer 2) (Layer 3) (Layer 4) (Layer 5) (QA) (Output)
Benefits: - ✅ Complete automation (single command) - ✅ Unix philosophy (composable layers) - ✅ Incremental processing - ✅ Error isolation per layer
When to Use: - Automated: 80% of use cases (workflow-multi-account) - Manual: Debugging, custom processing, layer-specific analysis
📊 Comparative Analysis¶
CLI vs Notebook Decision Matrix¶
| Criterion | CLI | Notebook | Recommendation |
|---|---|---|---|
| Speed | ⚡ Fast (seconds) | 🐌 Slower (minutes) | CLI for automation |
| Flexibility | 🔧 Structured | 🎨 Highly flexible | Notebook for exploration |
| Automation | ✅ Excellent (cron, Taskfile) | ⚠️ Moderate | CLI for pipelines |
| Visualization | ❌ Limited (tables) | ✅ Rich (charts, graphs) | Notebook for analysis |
| Learning Curve | 🟢 Low | 🟡 Medium | CLI for quick start |
| Team Sharing | ✅ Easy (scripts) | ⚠️ Requires Jupyter | CLI for collaboration |
| Reproducibility | ✅ Excellent | 🟡 Good (with version control) | CLI for consistency |
| Integration | ✅ Universal (bash, Python, etc.) | ⚠️ Python-specific | CLI for broad integration |
| Cost | 🟢 Free | 🟢 Free | Equal |
| Maintenance | ✅ Simple | ⚠️ Requires notebook management | CLI for long-term |
Overall Recommendation: - CLI-first for 80% of use cases (automation, scheduled tasks, production workflows) - Notebooks for 20% specialized analysis (exploratory analysis, custom visualizations, executive presentations)
Workflow Command Comparison¶
| Feature | workflow-multi-account |
workflow-single-account |
Manual Pipeline |
|---|---|---|---|
| Account Scope | All organization accounts | Single account | Configurable |
| Execution Time | 5-10 minutes (large orgs) | 1-2 minutes | Variable |
| Complexity | Low (automated) | Low (automated) | High (manual) |
| Customization | Limited | Limited | Unlimited |
| Error Handling | Automatic retry | Automatic retry | Manual |
| Use Case | Enterprise inventory | Focused analysis | Debugging, custom |
Recommendation: Use workflow-multi-account for 90% of enterprise use cases.
Resource Type Coverage¶
| Service | CLI Command | Notebook Support | MCP Validation | Coverage |
|---|---|---|---|---|
| EC2 | ✅ resource-explorer, discover-ec2 |
✅ Subprocess | ✅ ≥99.5% | 100% |
| RDS | ✅ discover-rds |
✅ Subprocess | ✅ ≥99.5% | 100% |
| Lambda | ✅ discover-lambda |
✅ Subprocess | ✅ ≥99.5% | 100% |
| WorkSpaces | ✅ discover-workspaces |
✅ Subprocess | ✅ ≥99.5% | 100% |
| Snapshots | ✅ discover-snapshots |
✅ Subprocess | ✅ ≥99.5% | 100% |
| S3 | ✅ list-s3-buckets |
✅ Subprocess | ✅ ≥99.5% | 100% |
| ELB | ✅ list-elbs |
✅ Subprocess | ✅ ≥99.5% | 100% |
| VPC | ✅ list-vpcs |
✅ Subprocess | ✅ ≥99.5% | 100% |
| Other 80 types | ✅ resource-explorer |
✅ Subprocess | ⚠️ Partial | 88 total |
Total Supported: 88 AWS resource types
🎓 Best Practices¶
1. Start with Workflows¶
# Begin with automated workflow, not individual commands
runbooks inventory workflow-multi-account --output inventory.csv
2. Validate Before Production¶
# Always validate accuracy before using data
runbooks inventory validate-mcp --input inventory.csv --output validation.json
runbooks inventory validate-costs --input inventory.csv --output cost-validation.json
3. Use Taskfile for Repeatability¶
# Store commands in Taskfile for team consistency
tasks:
daily-inventory:
cmds:
- runbooks inventory workflow-multi-account --output data/$(date +%Y%m%d)-inventory.csv
4. Export to Multiple Formats¶
# Generate both CSV (analysis) and Excel (presentations)
runbooks inventory export-excel --input inventory.csv --output report.xlsx
5. Leverage Notebooks for Exploration¶
# Use subprocess pattern for CLI integration in notebooks
subprocess.run(['runbooks', 'inventory', 'workflow-multi-account', '--output', '/tmp/data.csv'])
df = pd.read_csv('/tmp/data.csv')
📚 Additional Resources¶
- Quick Start Guide: quickstart.md - 5-minute getting started
- API Reference: CLI command documentation with all flags and options
- GitHub Repository: CloudOps-Runbooks
- PyPI Package: runbooks on PyPI
🆘 Troubleshooting¶
Issue: "No resources found"¶
Solution: Verify AWS credentials and account access
Issue: "MCP validation accuracy <99.5%"¶
Solution: Review discrepancies and check AWS API permissions
Issue: "Cost data missing"¶
Solution: Ensure Cost Explorer API is enabled and IAM permissions granted
Issue: "Workflow command timeout"¶
Solution: Use single-account workflow for large organizations or increase timeout
Document Version: v1.1.19 (November 2025) Maintained by: CloudOps Technical Documentation Team Feedback: Open GitHub issues for improvements