Huawei Cloud Cce Root Cause Analyzer

CCE Root Cause Analysis

⚠️ Execution Method (Must Read): This skill executes diagnosis via local Python scripts using the scripts/huawei-cloud.py dispatcher. Using hcloud, kubectl, or other CLI tools or direct API calls is prohibited.

• All actions are dispatched through scripts/huawei-cloud.py with --action and --params
• 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
• For action names and parameters, see the Core Tools section below
• Do not attempt hcloud, kubectl, curl IAM, or other CLI/API methods. This skill does not depend on these tools
• All paths are relative to the skill directory, which is the directory where this SKILL.md resides

Overview

This skill converges multi-domain evidence into root cause conclusions for CCE incidents. It orchestrates workload rollout diagnosis, dependency impact analysis, change impact analysis, AOM alarm analysis, and cross-domain drill-down (network, node) to produce a complete Markdown report with investigation steps, timeline, evidence chain, impact scope, Top3 root causes, confidence, counter-evidence, and remediation handoff.

This skill is applicable to the following scenarios:

1. Cross-resource incidents involving multiple failure domains (workload + dependency + change + alarm)
2. Root cause analysis when alarms span multiple CCE resources and the user needs comprehensive diagnosis
3. Correlating recent changes (deployments, config updates, network/security policy changes, node changes) with observed failures
4. Dependency impact propagation analysis (Service → Ingress → Pod → Node chain)
5. Workload rollout failures requiring evidence funnel (generation → ReplicaSet → Pod Ready → events → logs → command/args → probes → image)
6. Producing structured Top3 root cause reports with evidence, counter-evidence, and confidence scores

This skill does NOT handle the following:

1. Executing any remediation actions (scale, delete, drain, reboot, vulnerability state modification, cluster sleep/wake)
2. Making root cause conclusions from a single alarm without timeline or evidence chain
3. Creating, modifying, or deleting CCE resources
4. Guessing or fabricating diagnosis results without evidence

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Prerequisites

Before using, you must run the environment check script to complete environment validation and dependency installation in one step:

• Linux / macOS: skill action=exec: bash skill://scripts/check_env.sh
• Windows: skill action=exec: powershell -ExecutionPolicy Bypass -File skill://scripts/check_env.ps1

Windows Note: Do not use && to chain commands (PowerShell 5.x does not support it). Use semicolons if you need to change directories first.

The script will check in sequence: Python >= 3.6 → install dependencies → validate SDK → validate credentials → validate service availability. If the environment check fails, fix the issues before continuing with other actions.

Environment Variables:

Security Constraints:

1. Never persist credentials (AK/SK/Token/Certificate) to the filesystem
2. AK/SK exist only within the current request call stack; released after use
3. Only non-sensitive project IDs are cached in process memory (never written to disk)
4. All temporary certificate files must be deleted immediately after use
5. Never expose AK/SK in logs, responses, or error messages

Do not output the values of the above environment variables.

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IAM Permission Requirements

Permission Failure Handling:

1. When any command fails due to permission errors, display required permission list
2. Guide the user to create a custom policy in the IAM console
3. Pause execution and wait for user confirmation

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Core Tools

All actions are dispatched through scripts/huawei-cloud.py using skill action=exec:

Primary Comprehensive Diagnosis:

Workload Domain Actions:

Dependency and Impact Actions:

Change Impact Actions:

Network and Node Domain Actions:

Alarm and Report Actions:

Supporting Evidence Actions:

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Parameter Reference

Common Parameters:

*Required only for huawei_workload_rollout_diagnose.

Optional Parameters (passed via --params JSON):

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Output Format

Primary Comprehensive: huawei_root_cause_analyze

{
  "success": true,
  "analysis_trace_id": "RCA-...",
  "scope": {
    "region": "cn-north-4",
    "cluster_id": "cluster-id",
    "namespace": "optional",
    "target_name": "optional workload/app/service"
  },
  "summary": {
    "top_cause": {},
    "cause_count": 3,
    "data_sources": {
      "rollout": true,
      "dependency": true,
      "change": true,
      "alarms": true
    }
  },
  "top_causes": [
    {
      "rank": 1,
      "type": "ContainerCommandNotFound",
      "title": "New version container startup command or entry file does not exist",
      "domain": "workload",
      "confidence": 0.94,
      "evidence": [],
      "counter_evidence": [],
      "recommendation": [],
      "remediation_hint": {
        "skill": "huawei-cloud-cce-auto-remediation-runner",
        "action": "huawei_auto_remediation_run",
        "strategy": "rollback_previous_revision",
        "requires_confirmation": true
      }
    }
  ],
  "report_markdown": "# CCE Comprehensive Root Cause Analysis Report...",
  "report_file": "optional"
}

Supporting Domain Outputs

Each domain action (huawei_workload_rollout_diagnose, huawei_dependency_impact_analyze, huawei_change_impact_analyze, huawei_analyze_aom_alarms) produces its own structured JSON output. See individual skill documentation for domain-specific schemas.

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Verification

1. Run the environment check script to confirm dependencies and credentials are available
2. Use huawei_root_cause_analyze on a known healthy cluster to verify it returns success: true with zero or low-confidence causes
3. Use huawei_root_cause_analyze on a cluster with known multi-domain failures to verify Top3 causes are accurately identified
4. Compare huawei_root_cause_analyze summary with individual domain action outputs for consistency
5. Verify that evidence chains reference specific objects, events, and API fields (not generic statements)
6. Verify that counter-evidence is present for each top cause candidate
7. Confirm that low-confidence conclusions are clearly labeled with required supplementary data

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Best Practices

1. Always start with huawei_root_cause_analyze for comprehensive diagnosis; drill down into individual domain actions only when specific evidence requires deeper analysis
2. For workload rollout failures, prioritize the rollout funnel: generation/observedGeneration → ReplicaSet → Pod Ready → Events → Logs → command/args → probes → image
3. For service unavailability, use huawei_dependency_impact_analyze to trace Service selector → Ingress backend → Pod Ready → Node distribution propagation paths
4. For suspected change-induced failures, use huawei_change_impact_analyze to build "change occurred before failure" causal chain with audit logs, K8s historical events, and AOM alarms
5. Never conclude root cause from a single alarm alone; always provide timeline or evidence chain
6. Record supporting evidence, counter-evidence, data gaps, and remediation handoff for each root cause candidate
7. Sort root causes by impact scope, timeline alignment, evidence strength, and recoverability
8. Clearly label low-confidence conclusions with required supplementary data
9. All remediation actions must be output as recommendations only and handed off to huawei-cloud-cce-auto-remediation-runner

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Reference Documents

• Evidence chain and root cause ranking workflow: references/workflow.md
• Output structure specification: references/output-schema.md
• Risk boundaries and handoff rules: references/risk-rules.md
• Huawei Cloud CCE Documentation
• Huawei Cloud Python SDK Documentation

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Notes

1. This skill is read-only diagnosis and report generation only; no write, scale, delete, cordon, drain, reboot, vulnerability state modification, or cluster sleep/wake operations
2. Do not output the values of HW_ACCESS_KEY, HW_SECRET_KEY, HW_SECURITY_TOKEN, or other environment variables
3. All scripts must be executed via skill action=exec; do not run them directly in a shell
4. Any action requiring confirm=true must be handed off to huawei-cloud-cce-auto-remediation-runner; this skill never executes remediation
5. The environment check script must be run before any diagnosis action
6. When using temporary AK/SK, HW_SECURITY_TOKEN must be set

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Common Pitfalls

1. Concluding root cause from a single alarm — Always require timeline or evidence chain; a single alarm without temporal correlation is insufficient evidence
2. Skipping huawei_root_cause_analyze and drilling into individual domains first — Always start with comprehensive analysis; individual domain drill-down is for supplementary evidence only
3. Ignoring counter-evidence — Each root cause candidate must include counter-evidence and data gaps; omitting these leads to false confidence
4. Not building a fault timeline — Establish user-perceived time, alarm trigger time, Kubernetes event time, and change time before ranking causes
5. Attempting remediation actions from this skill — All changes must be handed off to huawei-cloud-cce-auto-remediation-runner; this skill only outputs recommendations
6. Failing to label low-confidence conclusions — When evidence is insufficient, write "insufficient evidence" explicitly; never present guesses as conclusions
7. Not correlating changes with failures — When a recent deployment, config, network, or security policy change exists, use huawei_change_impact_analyze to verify the "change before failure" causal chain
8. Treating dependency propagation as single-direction — Dependency impact can propagate bidirectionally (upstream failure affects downstream, and downstream back-pressure affects upstream); analyze both directions