Huawei Cloud CCE Ops Report Generator#

⚠️ 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#

SkillPurposehuawei-cloud-cce-daily-cluster-inspectorDaily health inspection and risk summarizationhuawei-cloud-cce-capacity-trend-forecasterCapacity trend analysis, bottleneck forecasting, HPA simulationhuawei-cloud-cce-availability-risk-scannerAvailability risk scanning (single replicas, PDB, AZ distribution)huawei-cloud-cce-cost-optimization-advisorCost optimization analysis (idle resources, oversized requests)huawei-cloud-cce-auto-remediation-runnerExecute 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#

VariableRequiredDescriptionHUAWEI_AKYesHuawei Cloud Access KeyHUAWEI_SKYesHuawei Cloud Secret KeyHUAWEI_REGIONNoDefault region (overrides region param if set)HUAWEI_PROJECT_IDNoProject ID (auto-obtained via IAM API when not set)HUAWEI_SECURITY_TOKENNoRequired 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-SkillRequired Permissionshuawei-cloud-cce-daily-cluster-inspectorCCE cluster/node/workload/event read, AOM alarm readhuawei-cloud-cce-capacity-trend-forecasterCCE cluster/node/nodepool/Deployment/HPA read, AOM metrics readhuawei-cloud-cce-availability-risk-scannerCCE cluster/node/workload/PDB/Service/Ingress read, AOM metrics readhuawei-cloud-cce-cost-optimization-advisorCCE 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:

ActionSource SkillDescriptionhuawei_cce_auto_inspection``huawei-cloud-cce-daily-cluster-inspectorFull daily health inspectionhuawei_analyze_cce_capacity_trend``huawei-cloud-cce-capacity-trend-forecasterCapacity trend analysis with simulationhuawei_scan_cce_availability_risk``huawei-cloud-cce-availability-risk-scannerAvailability risk scan with remediation planhuawei_analyze_cce_cost_optimization``huawei-cloud-cce-cost-optimization-advisorCost optimization analysis

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Parameter Reference#

huawei_generate_ops_report (Primary Action)#

ParameterRequiredDefaultDescriptionregionYes-Huawei Cloud region (e.g., cn-north-4)cluster_idYes-CCE cluster IDreport_typeNoweeklyReport type: weekly, monthly, sla, capacity, stability``hoursNoAuto (by type)Analysis window in hours (overrides default for report type)short_hoursNo24Short-period lookback for cost analysislong_hoursNo168Long-period lookback for cost analysisexclude_namespacesNokube-systemComma-separated namespaces excluded from business analysisbusiness_namespacesNo-Comma-separated namespace allowlist for business Deploymentsgateway_keywordsNonginx,gateway,ingress,proxy,kong,apisix,traefikKeywords for identifying gateway-class workloadsoutput_dirNo-Directory to persist Markdown, HTML, JSON reports and chartsinclude_rawNofalseInclude raw source payloads for traceabilityoncall_report_pathNo-Path to on-call report file for incident contextoncall_summaryNo-Inline on-call summary text for incident context

Default Hours by Report Type#

Report TypeDefault Hoursweekly168monthly744sla168capacity168stability168

Common Region IDs#

Region NameRegion IDNorth China - Beijing 4cn-north-4North China - Beijing 1cn-north-1East China - Shanghai 1cn-east-3East China - Shanghai 2cn-east-2South China - Guangzhoucn-south-1South China - Shenzhencn-south-4Southwest China - Guiyang 1cn-southwest-2Asia Pacific - Bangkokap-southeast-2Asia Pacific - Singaporeap-southeast-1Asia Pacific - Hong Kongap-southeast-3Europe - Pariseu-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:

FileDescriptionops-<type>-summary.jsonFull structured JSON output with summaries, recommendations, and sourcesops-<type>-report.mdMarkdown report with cross-source analysis and recommendationsops-<type>-report.htmlHTML report with embedded SVG trend chartsops-capacity-trend.svgCapacity trend curve chart (when capacity data available)ops-capacity-simulation.svgCapacity simulation chart (when simulation data available)ops-<type>-raw.jsonRaw source payloads (when include_raw=true)

Key Output Fields#

FieldDescriptionscopeReport scope: region, cluster_id, excluded namespaces, gateway keywordsreportReport metadata: type, hours, short_hours, long_hourssummary.daily_cluster_inspectorHealth status and anomaly countsummary.capacity_trend_forecasterCPU/memory averages, trend direction, simulation statussummary.availability_risk_scannerRisk level and issue countsummary.cost_optimization_advisorUnderutilized nodes and oversized requests countsummary.oncall_copilotOn-call context status, source, and summaryrecommendationsPrioritized cross-source recommendation list with source and risk leveldata_gapsList of sources that failed or had missing datasourcesPer-source success status and file pathsfilesOutput file paths for all generated artifacts

---

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

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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#

DocumentDescriptionreferences/workflow.mdDetailed execution process, scope validation, and data gap handlingreferences/output-schema.mdComplete 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#

PitfallCorrect ApproachAssuming report fails when one source failsThe report continues with degraded sources; data gaps are explicitly listedSkipping on-call context entirely when unavailableMark as a context gap; the report still provides value from other sourcesExecuting remediation directly from report findingsAll remediation requires explicit user authorization; hand off to huawei-cloud-cce-auto-remediation-runnerNot using output_dir for persistent reportsAlways specify output_dir for audit traceability and stakeholder accessTreating recommendations as action items without risk contextEach recommendation includes [source][risk_level] prefix; prioritize by risk levelIgnoring include_raw for compliance requirementsUse include_raw=true when audit traceability of source data is requiredUsing wrong report_type for the reporting cycleMatch report type to cycle: weekly=168h, monthly=744h, sla/capacity/stability=168h by default