Huawei Cloud Cce Observability Context Builder

Collect and consolidate Huawei Cloud CCE alarms, metrics, logs, and events into a comprehensive observability context package for diagnosis handoff.

⚠️ Execution Method (Must Read): This skill executes actions 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 <action_name> and --params <json_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 consolidates scattered fault signals into a structured, diagnosable context package. It first collects the time window, cluster, namespace, workload, Pod, node, and alarm scope, then gathers evidence by type (alarms, events, metrics, logs), merges signals along a timeline, and identifies gaps and the appropriate next diagnostic skill for hand-off. This skill is strictly read-only — it never executes remediation actions.

Architecture: python3 scripts/huawei-cloud.py dispatcher → Huawei Cloud Python SDK + AOM/LTS API → alarms, metrics, logs, events aggregation

Related Skills:

• huawei-cloud-cce-pod-failure-diagnoser - Pod CrashLoopBackOff, ImagePullBackOff, OOMKilled diagnosis
• huawei-cloud-cce-node-failure-diagnoser - Node health, resource pressure, NPD events diagnosis
• huawei-cloud-cce-network-failure-diagnoser - Network connectivity, DNS, ELB diagnosis
• huawei-cloud-cce-storage-failure-diagnoser - PVC/PV mount, storage provisioning diagnosis
• huawei-cloud-cce-root-cause-analyzer - Cross-domain root cause analysis and reports
• huawei-cloud-cce-auto-remediation-runner - Remediation actions (scale, drain, rollback, etc.)
• huawei-cloud-cce-alarm-correlation-engine - Alarm deduplication and correlation
• huawei-cloud-cce-metric-analyzer - Deep metric trend analysis
• huawei-cloud-cce-log-analyzer - Deep log pattern analysis

Capabilities:

• Collect active and history AOM alarms, deduplicate and group by severity (huawei_list_aom_alarms, huawei_analyze_aom_alarms)
• Retrieve Kubernetes Events grouped by object and reason (huawei_get_cce_events)
• Query Pod and Node TopN metrics for resource peaks and anomalies (huawei_get_cce_pod_metrics_topN, huawei_get_cce_node_metrics_topN)
• Query AOM and LTS logs for deep log evidence (huawei_query_aom_logs, huawei_get_recent_logs)
• Fetch Pod-side container logs (huawei_get_pod_logs)
• Get AOM metrics and instance list (huawei_get_aom_metrics, huawei_list_aom_instances)
• Generate monitor dashboards from collected data (huawei_generate_monitor_dashboard)
• Merge signals along a timeline, mark gaps, and recommend the next diagnostic skill

Typical Use Cases:

• "Collect all observability data for cluster xyz in the last hour"
• "Build a context package for a Pod crash incident"
• "Gather alarms, events, and metrics before diagnosis"
• "I see multiple alarms, consolidate them into a diagnosis context"
• "Show me the full observability picture: alarms + metrics + logs + events"
• "What's happening in namespace prod over the last 30 minutes?"

Prerequisites

1. Python Dependencies

• Python 3.8+ with huaweicloudsdkcce, huaweicloudsdkcore, huaweicloudsdkaom, huaweicloudsdklts packages
• Run environment check before first use (see Verification section)

2. Credential Configuration

• Valid Huawei Cloud credentials (AK/SK mode)
• Security Rules:
  • Never expose AK/SK values in code, conversation, or commands
  • Never use echo $HUAWEI_AK or echo $HUAWEI_SK to check credentials
  • Use environment variables: HUAWEI_AK, HUAWEI_SK, HUAWEI_REGION
  • Prefer IAM users over root account for cloud operations
  • Enable MFA for sensitive operations

Configuration Method (Environment Variables Only):

bash

export HUAWEI_AK=<your-ak>
export HUAWEI_SK=<your-sk>
export HUAWEI_REGION=cn-north-4

Important Security Notes:

• Never commit credentials to version control
• Use IAM users with minimal required permissions
• Rotate AK/SK regularly

3. IAM Permission Requirements

API Action	Permission	Purpose
cce:cluster:get	Get cluster	View CCE cluster details
cce:cluster:createCert	Create certificate	Obtain kubeconfig for kubectl access
aom:alarm:list	List alarms	Query AOM active/history alarms
aom:alarm:analyze	Analyze alarms	Deduplicate and group alarms
aom:metricsData:get	Get metrics data	Query Pod/node CPU/memory metrics
aom:instance:list	List AOM instances	Discover AOM Prom instance
aom:logData:get	Get log data	Query AOM/LTS log data
lts:log:list	List LTS logs	Query LTS log streams
cce:event:list	List events	Query Kubernetes Events

Permission Failure Handling:

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

Core Tools

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

Alarm Collection

Action	Required Parameters	Description
huawei_list_aom_alarms	region, cluster_id	Collect active + history AOM alarms for the cluster
huawei_analyze_aom_alarms	region, cluster_id	Deduplicate alarms and group by severity level

Event and Metric Collection

Action	Required Parameters	Description
huawei_get_cce_events	region, cluster_id	Retrieve Kubernetes Events grouped by object and reason
huawei_get_cce_pod_metrics_topN	region, cluster_id, namespace	TopN Pod metrics (CPU/memory) for anomaly detection
huawei_get_cce_node_metrics_topN	region, cluster_id	TopN Node metrics for resource pressure detection
huawei_get_aom_metrics	region, cluster_id, namespace	Query AOM metrics for specific resources
huawei_list_aom_instances	region	Discover AOM Prom instance for metrics queries

Log Collection

Action	Required Parameters	Description
huawei_query_aom_logs	region, cluster_id, namespace	Query AOM structured log data
huawei_get_recent_logs	region, cluster_id, namespace	Get recent log entries (LTS)
huawei_get_pod_logs	region, cluster_id, pod_name, namespace	Fetch Pod container logs (previous or current)

Visualization

Action	Required Parameters	Description
huawei_generate_monitor_dashboard	region, cluster_id	Generate monitoring dashboard from collected data

Parameter Reference

Common Parameters

Parameter	Required	Description	Default
region	Yes	Huawei Cloud region	HUAWEI_REGION
cluster_id	Yes	CCE cluster ID	N/A
namespace	No	Kubernetes namespace	N/A
ak	Optional	Override AK	HUAWEI_AK
sk	Optional	Override SK	HUAWEI_SK
project_id	Optional	Project ID	Auto from IAM

Alarm Collection Parameters

Parameter	Required	Description	Default
alarm_id	No	Specific alarm ID to query	N/A
alarm_level	No	Alarm severity filter	All
hours	No	History lookback window (hours)	1

Log Collection Parameters

Parameter	Required	Description	Default
pod_name	Yes*	Pod name (for huawei_get_pod_logs)	N/A
container	No	Container name	First
previous	No	Fetch previous (crashed) logs	false
tail_lines	No	Number of log tail lines	100

Metric Collection Parameters

Parameter	Required	Description	Default
top_n	No	Number of top results	10
hours	No	Metrics lookback window (hours)	1

*Required for specific actions as noted.

Workflow

1. Record scope: Capture fault time, region, cluster_id, namespace, workload, pod, node, and alarm_id provided by the user
2. Set time window: If time is unclear, default to the last 1 hour and note this assumption in the output
3. Collect alarms: Call huawei_list_aom_alarms to collect active + history alarms, then use huawei_analyze_aom_alarms for deduplication and severity grouping
4. Collect events: Call huawei_get_cce_events to retrieve Kubernetes Events grouped by involved object and reason
5. Collect metrics: Call Pod/Node TopN metrics tools to find resource peaks, abnormal nodes, and abnormal Pods
6. Collect logs: When log evidence is needed, prefer huawei_query_aom_logs, then supplement with Pod-side logs from huawei_get_recent_logs or huawei_get_pod_logs
7. Merge and output: Merge signals along a timeline, output the anomaly summary, missing information (gaps), and recommended diagnostic skill for hand-off

For the complete evidence-gathering workflow, see references/workflow.md.

Output Format

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

Context Package Output:

json

{
  "summary": "one paragraph context summary",
  "scope": {
    "region": "cn-north-4",
    "cluster_id": "optional",
    "namespace": "optional",
    "workload": "optional",
    "time_window": "optional"
  },
  "signals": {
    "alarms": [],
    "events": [],
    "metrics": [],
    "logs": []
  },
  "timeline": [],
  "gaps": [],
  "next_skill": "huawei-cloud-cce-pod-failure-diagnoser | huawei-cloud-cce-node-failure-diagnoser | huawei-cloud-cce-network-failure-diagnoser | huawei-cloud-cce-root-cause-analyzer"
}

Key output fields:

• summary — one paragraph summarizing the collected observability context
• scope — region, cluster, namespace, workload, and time window
• signals — collected evidence grouped by type (alarms, events, metrics, logs)
• timeline — merged signal timeline showing event chronology
• gaps — missing data that could improve diagnosis
• next_skill — recommended diagnostic skill for hand-off based on signal analysis

Risk Rules

This skill is strictly read-only observability — no mutations allowed.

• Allow automatic R1 read-only queries: alarms, metrics, logs, events, inventory, read-only report generation
• Prohibit any action requiring confirm=true — no mutations allowed
• Never persist AK/SK, tokens, certificates, or kubeconfig
• Log output must be sanitized. When suspected secrets are found, describe the hit location only — never copy the original text
• Charts and reports must only be generated from authorized query results

For complete risk classification, see references/risk-rules.md.

Verification

1. Run python3 scripts/huawei-cloud.py huawei_list_aom_alarms region=cn-north-4 cluster_id=<cluster-id> to verify alarm query connectivity
2. Run python3 scripts/huawei-cloud.py huawei_get_cce_events region=cn-north-4 cluster_id=<cluster-id> limit=10 to verify Event query works
3. Run python3 scripts/huawei-cloud.py huawei_get_cce_pod_metrics_topN region=cn-north-4 cluster_id=<cluster-id> namespace=default top_n=5 to verify metrics TopN
4. Run a full context build on a healthy namespace and confirm the output contains valid scope, signals, timeline, gaps, and next_skill fields
5. Verify that no mutation actions are suggested in the output — all actions should be read-only or hand-offs to diagnosis skills

Best Practices

1. Always start with alarms: Check active + history alarms first using huawei_list_aom_alarms and huawei_analyze_aom_alarms — alarms provide the most direct fault signals
2. Define scope early: Record region, cluster_id, namespace, workload, pod, node, and time window before collecting any data. If the time window is unclear, default to the last 1 hour and note the assumption
3. Use TopN for quick anomaly detection: huawei_get_cce_pod_metrics_topN and huawei_get_cce_node_metrics_topN efficiently highlight resource peaks without scanning all resources
4. Prefer AOM logs first, then supplement with Pod logs: huawei_query_aom_logs provides structured log data; use huawei_get_pod_logs or huawei_get_recent_logs for Pod-side container log details
5. Merge signals along a timeline: Chronological merging of alarms, events, metrics, and logs reveals causal chains that individual data types cannot
6. Mark gaps explicitly: Always identify missing data in the gaps field — this guides the next diagnostic skill on what additional evidence to collect
7. Never suggest mutation actions: This skill is read-only. For scaling, deletion, restart, drain, or vulnerability state changes, hand off to huawei-cloud-cce-auto-remediation-runner
8. Recommend the correct next skill: Based on signal analysis, recommend the most specific diagnoser — Pod failures → huawei-cloud-cce-pod-failure-diagnoser, node issues → huawei-cloud-cce-node-failure-diagnoser, network → huawei-cloud-cce-network-failure-diagnoser, cross-domain → huawei-cloud-cce-root-cause-analyzer

Reference Documents

Document	Description
Workflow	Evidence-gathering workflow and step sequence
Risk Rules	Safety constraints and risk classification
Output Schema	JSON response format for context package

Notes

1. This skill is strictly read-only — it never executes remediation actions. For mutation actions, hand off to huawei-cloud-cce-auto-remediation-runner
2. All actions are R1 (read-only) — no confirm=true is ever needed
3. Log excerpts are sanitized — suspected passwords, tokens, AK/SK, and Authorization headers are redacted in output
4. AK/SK must never be hardcoded — use environment variables only
5. The Python dispatcher script (scripts/huawei-cloud.py) is the only execution method — do not use hcloud CLI or direct API calls
6. The next_skill field in the output uses huawei-cloud-cce-* naming for cross-skill hand-off
7. When alarm correlation is needed before context building, consider using huawei-cloud-cce-alarm-correlation-engine

Common Pitfalls

Pitfall	Symptom	Quick Fix
Missing cluster_id	All actions fail immediately	Provide cluster_id from cluster list
No time window specified	Broad, noisy results	Default to last 1 hour; note assumption in output
Skipping alarm collection	Missing critical fault signals	Always start with huawei_list_aom_alarms
Not merging signals on timeline	Isolated data points, no causal chain	Chronologically merge alarms, events, metrics
Suggesting mutation actions	Unsafe recommendations	All mutations → huawei-cloud-cce-auto-remediation-runner
Not marking data gaps	Diagnosis skill lacks direction	Always populate the gaps field
Querying all namespaces	Slow response, too many results	Scope with namespace and workload
AOM Prom instance not found	Metrics queries return empty	Verify with huawei_list_aom_instances first