Huawei Cloud Cce Ops Report Generator

Use when generating consolidated CCE operations reports that combine daily inspection, capacity trend, availability risk, cost optimization, and on-call context into weekly, monthly, SLA, capacity, or stability reports with Markdown and HTML output. Trigger: user mentions "ops report", "运维报告", "report generation", "报告生成", "cluster report", "集群报告", "weekly report", "月度报告", "SLA report", "capacity report", "stability report", "operations summary", "运营总结", "consolidated report", "综合报告", "CCE reporting", "oncall report", "值班报告"

⚠️ Execution Method (Must Read): This skill executes queries via the local Python dispatcher script. Using hcloud, openstack, or other CLI tools or direct API calls is prohibited.

• The dispatcher script is located at scripts/huawei-cloud.py within the skill directory
• All scripts and environment check scripts are inside the skill package. You must use skill action=exec to execute them. Do not run them directly in a shell.
• Do not attempt hcloud, openstack, curl IAM, or any other CLI/API methods. This skill does not depend on those tools.
• All paths are relative to the skill directory, which is the directory where this SKILL.md is located.

Overview

This skill generates consolidated operations reports for Huawei Cloud CCE clusters. It aggregates outputs from daily inspection, capacity trend forecasting, availability risk scanning, cost optimization analysis, and optional on-call context into structured reports (weekly, monthly, SLA, capacity, stability). Default behavior is read-only analysis and report generation.

Architecture: Python dispatcher (scripts/huawei-cloud.py) → huawei_generate_ops_report → aggregates from huawei-cloud-cce-daily-cluster-inspector, huawei-cloud-cce-capacity-trend-forecaster, huawei-cloud-cce-availability-risk-scanner, huawei-cloud-cce-cost-optimization-advisor, on-call context → Markdown + HTML + JSON reports

Related Skills

Skill	Purpose
huawei-cloud-cce-daily-cluster-inspector	Daily health inspection and risk summarization
huawei-cloud-cce-capacity-trend-forecaster	Capacity trend analysis, bottleneck forecasting, HPA simulation
huawei-cloud-cce-availability-risk-scanner	Availability risk scanning (single replicas, PDB, AZ distribution)
huawei-cloud-cce-cost-optimization-advisor	Cost optimization analysis (idle resources, oversized requests)
huawei-cloud-cce-auto-remediation-runner	Execute remediation actions when user authorizes

Capabilities

1. One-shot consolidated ops report generation (huawei_generate_ops_report)
2. Five report types: weekly, monthly, sla, capacity, stability
3. Aggregated summaries from inspection, capacity, availability, cost, and on-call sources
4. Prioritized cross-source recommendations with risk levels
5. Markdown and HTML output with trend charts when capacity data is available
6. Optional raw payload traceability (include_raw=true)
7. Graceful degradation: reports continue even if individual sources fail, marking data gaps clearly

Typical Use Cases

• "Generate a weekly ops report for my CCE cluster"
• "Create a monthly operations summary with capacity and availability findings"
• "Produce an SLA report combining inspection and on-call data"
• "Consolidate capacity trends and cost optimization into a stability report"
• "Generate a report with on-call context from last week's incidents"
• "Export a consolidated ops report in HTML with charts"

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Prerequisites

Python Dependencies

The dispatcher script requires Python >= 3.6 and the following packages:

• huaweicloudsdkcore
• huaweicloudsdkcce
• huaweicloudsdkaom
• huaweicloudsdkhss
• huaweicloudsdkvpc
• huaweicloudsdkecs
• huaweicloudsdkces
• huaweicloudsdkevs
• huaweicloudsdkeip
• huaweicloudsdkelb
• huaweicloudsdkiam
• kubernetes
• matplotlib
• numpy

Install: pip3 install huaweicloudsdkcore huaweicloudsdkcce huaweicloudsdkaom huaweicloudsdkhss huaweicloudsdkvpc huaweicloudsdkecs huaweicloudsdkces huaweicloudsdkevs huaweicloudsdkeip huaweicloudsdkelb huaweicloudsdkiam kubernetes matplotlib numpy

Credential Configuration

Variable	Required	Description
HUAWEI_AK	Yes	Huawei Cloud Access Key
HUAWEI_SK	Yes	Huawei Cloud Secret Key
HUAWEI_REGION	No	Default region (overrides region param if set)
HUAWEI_PROJECT_ID	No	Project ID (auto-obtained via IAM API when not set)
HUAWEI_SECURITY_TOKEN	No	Required when using temporary AK/SK

🚫 Never expose or log AK/SK values. Credentials exist only in the current request call stack and are released after each invocation. Do not write credentials to files, logs, or responses.

✅ Use environment variables HUAWEI_AK / HUAWEI_SK for authentication. The dispatcher reads them automatically.

IAM Permission Requirements

This skill aggregates data from multiple sub-skills. It requires all permissions needed by:

Sub-Skill	Required Permissions
huawei-cloud-cce-daily-cluster-inspector	CCE cluster/node/workload/event read, AOM alarm read
huawei-cloud-cce-capacity-trend-forecaster	CCE cluster/node/nodepool/Deployment/HPA read, AOM metrics read
huawei-cloud-cce-availability-risk-scanner	CCE cluster/node/workload/PDB/Service/Ingress read, AOM metrics read
huawei-cloud-cce-cost-optimization-advisor	CCE cluster/node/nodepool/Deployment/HPA/Pod read, AOM metrics read

---

Core Commands

All actions are invoked via the dispatcher script:

bash

python3 scripts/huawei-cloud.py <action> region=<region> cluster_id=<cluster_id> [key=value ...]

1. Primary Action: Generate Ops Report

The primary action aggregates all sub-skill outputs into a consolidated report:

bash

python3 scripts/huawei-cloud.py huawei_generate_ops_report \
  region=cn-north-4 cluster_id=<cluster_id> \
  report_type=weekly \
  output_dir=./output

Returns: consolidated summary, cross-source recommendations, data gaps, and output files (Markdown, HTML, JSON, optional SVG charts).

2. Supporting Context Actions

For follow-up deep dives after report generation:

Action	Source Skill	Description
huawei_cce_auto_inspection	huawei-cloud-cce-daily-cluster-inspector	Full daily health inspection
huawei_analyze_cce_capacity_trend	huawei-cloud-cce-capacity-trend-forecaster	Capacity trend analysis with simulation
huawei_scan_cce_availability_risk	huawei-cloud-cce-availability-risk-scanner	Availability risk scan with remediation plan
huawei_analyze_cce_cost_optimization	huawei-cloud-cce-cost-optimization-advisor	Cost optimization analysis

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Parameter Reference

huawei_generate_ops_report (Primary Action)

Parameter	Required	Default	Description
region	Yes	-	Huawei Cloud region (e.g., cn-north-4)
cluster_id	Yes	-	CCE cluster ID
report_type	No	weekly	Report type: weekly, monthly, sla, capacity, stability
hours	No	Auto (by type)	Analysis window in hours (overrides default for report type)
short_hours	No	24	Short-period lookback for cost analysis
long_hours	No	168	Long-period lookback for cost analysis
exclude_namespaces	No	kube-system	Comma-separated namespaces excluded from business analysis
business_namespaces	No	-	Comma-separated namespace allowlist for business Deployments
gateway_keywords	No	nginx,gateway,ingress,proxy,kong,apisix,traefik	Keywords for identifying gateway-class workloads
output_dir	No	-	Directory to persist Markdown, HTML, JSON reports and charts
include_raw	No	false	Include raw source payloads for traceability
oncall_report_path	No	-	Path to on-call report file for incident context
oncall_summary	No	-	Inline on-call summary text for incident context

Default Hours by Report Type

Report Type	Default Hours
weekly	168
monthly	744
sla	168
capacity	168
stability	168

Common Region IDs

Region Name	Region ID
North China - Beijing 4	cn-north-4
North China - Beijing 1	cn-north-1
East China - Shanghai 1	cn-east-3
East China - Shanghai 2	cn-east-2
South China - Guangzhou	cn-south-1
South China - Shenzhen	cn-south-4
Southwest China - Guiyang 1	cn-southwest-2
Asia Pacific - Bangkok	ap-southeast-2
Asia Pacific - Singapore	ap-southeast-1
Asia Pacific - Hong Kong	ap-southeast-3
Europe - Paris	eu-west-0

---

Output Format

See references/output-schema.md for the complete JSON response schema.

Output Files

When output_dir is specified, the following files are generated:

File	Description
ops-<type>-summary.json	Full structured JSON output with summaries, recommendations, and sources
ops-<type>-report.md	Markdown report with cross-source analysis and recommendations
ops-<type>-report.html	HTML report with embedded SVG trend charts
ops-capacity-trend.svg	Capacity trend curve chart (when capacity data available)
ops-capacity-simulation.svg	Capacity simulation chart (when simulation data available)
ops-<type>-raw.json	Raw source payloads (when include_raw=true)

Key Output Fields

Field	Description
scope	Report scope: region, cluster_id, excluded namespaces, gateway keywords
report	Report metadata: type, hours, short_hours, long_hours
summary.daily_cluster_inspector	Health status and anomaly count
summary.capacity_trend_forecaster	CPU/memory averages, trend direction, simulation status
summary.availability_risk_scanner	Risk level and issue count
summary.cost_optimization_advisor	Underutilized nodes and oversized requests count
summary.oncall_copilot	On-call context status, source, and summary
recommendations	Prioritized cross-source recommendation list with source and risk level
data_gaps	List of sources that failed or had missing data
sources	Per-source success status and file paths
files	Output file paths for all generated artifacts

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Workflow

1. Collect region, cluster_id, report type, time window, namespace scope, and output directory from user
2. Execute huawei_generate_ops_report — it aggregates from all five sources internally
3. Review generated Markdown report first, then HTML report for charts and visualization
4. For high-risk findings, trace back to source sections:
   • Daily anomalies → huawei-cloud-cce-daily-cluster-inspector
   • Risk level and issue categories → huawei-cloud-cce-availability-risk-scanner
   • Oversized requests / low utilization → huawei-cloud-cce-cost-optimization-advisor
   • Trend slope / bottleneck projection → huawei-cloud-cce-capacity-trend-forecaster
5. If customer asks for remediation, switch to explicit change workflow and require authorization — hand off to huawei-cloud-cce-auto-remediation-runner

Data Gap Handling

• If a source report fails, the aggregate report continues and marks that source as degraded
• If on-call input is missing, mark as a context gap instead of failing the report
• Preserve source file pointers and optional raw payloads (include_raw=true) for auditability

---

Verification

1. Run a weekly report with a known cluster:

   bash

   python3 scripts/huawei-cloud.py huawei_generate_ops_report \
     region=cn-north-4 cluster_id=<cluster-id> \
     report_type=weekly output_dir=./output
   

2. Verify success=true and all four source summaries are present
3. Check that recommendations lists cross-source items with [source][risk_level] prefixes
4. Verify data_gaps is empty when all sources succeed
5. Confirm ops-weekly-report.md and ops-weekly-report.html are generated in output_dir
6. Test with include_raw=true and verify ops-weekly-raw.json contains source payloads
7. Test with oncall_summary="Test incident" and verify on-call context appears in summary

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Best Practices

1. Always use huawei_generate_ops_report as the primary action; it aggregates all sources in one call
2. Choose report type matching the reporting cycle: weekly for weekly reviews, monthly for monthly summaries, sla for SLA tracking, capacity for capacity planning, stability for stability assessment
3. Use output_dir to persist reports for audit and stakeholder review
4. For traceability, use include_raw=true to preserve source payloads
5. If on-call context is available, pass it via oncall_report_path or oncall_summary to enrich the report
6. For high-risk findings, trace back to the relevant sub-skill for detailed remediation plans
7. Do NOT execute write actions (HPA apply, scale, node pool resize, workload mutation) unless the user explicitly authorizes remediation — hand off to huawei-cloud-cce-auto-remediation-runner

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Reference Documents

Document	Description
references/workflow.md	Detailed execution process, scope validation, and data gap handling
references/output-schema.md	Complete JSON response schema for report output

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Notes

• Read-only by design — this skill does NOT modify workloads, HPA, node pools, or cluster configuration
• Remediation hand-off — all mutation suggestions are handed off to huawei-cloud-cce-auto-remediation-runner with user authorization
• Never expose or log AK/SK or environment variable values
• All actions are executed via python3 scripts/huawei-cloud.py <action>; do not use hcloud CLI or direct API calls
• Data gaps — the report continues generation even when individual sources fail; gaps are clearly marked
• On-call optional — when on-call context is unavailable, the report still generates with a marked context gap

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Common Pitfalls

Pitfall	Correct Approach
Assuming report fails when one source fails	The report continues with degraded sources; data gaps are explicitly listed
Skipping on-call context entirely when unavailable	Mark as a context gap; the report still provides value from other sources
Executing remediation directly from report findings	All remediation requires explicit user authorization; hand off to huawei-cloud-cce-auto-remediation-runner
Not using output_dir for persistent reports	Always specify output_dir for audit traceability and stakeholder access
Treating recommendations as action items without risk context	Each recommendation includes [source][risk_level] prefix; prioritize by risk level
Ignoring include_raw for compliance requirements	Use include_raw=true when audit traceability of source data is required
Using wrong report_type for the reporting cycle	Match report type to cycle: weekly=168h, monthly=744h, sla/capacity/stability=168h by default