Huawei Cloud Cce Capacity Trend Forecaster

Use when analyzing Huawei Cloud CCE periodic capacity trends, forecasting resource bottlenecks, simulating node/workload elasticity policies, generating capacity curve charts and reports, comparing recurring history records, or tuning HPA and node autoscaler configurations. Trigger: user mentions "capacity forecast", "容量预测", "capacity trend", "容量趋势", "resource trend", "资源趋势", "capacity planning", "容量规划", "capacity risk", "容量风险", "resource exhaustion", "资源耗尽", "HPA tuning", "node autoscaler", "capacity report", "capacity simulation"

shijingcheng@pintudeyudi

Install

openclaw skills install @pintudeyudi/huawei-cloud-cce-capacity-trend-forecaster

Huawei Cloud CCE Capacity Trend Forecaster

⚠️ 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 analyzes CCE capacity trends over a user-selected period (1 hour to 1 month), forecasts resource bottlenecks, simulates elasticity policies, generates curve charts and reports, and compares recurring history records. The core principle is to treat capacity as a time-series problem: rising trends with high p95 signal risk, low utilization with conservative simulation signals cost optimization opportunity.

This skill has read-only tools (collection, analysis, simulation, report generation) and preview tools for HPA and autoscaler configuration. Applying configuration changes requires a two-step confirmation workflow with confirm=true and explicit user approval.

Related Skills

Skill	Purpose
`huawei-cloud-cce-metric-analyzer`	Query Pod/Node CPU/memory/disk metrics and anomaly detection
`huawei-cloud-cce-node-failure-diagnoser`	Node failure diagnosis (NotReady, resource pressure, NPD events)
`huawei-cloud-cce-pod-failure-diagnoser`	Pod-level failure diagnosis (CrashLoopBackOff, ImagePullBackOff, etc.)
`huawei-cloud-cce-workload-failure-diagnoser`	Workload-level failure diagnosis
`huawei-cloud-cce-auto-remediation-runner`	Execute remediation actions (scale, reboot, drain)
`huawei-cloud-cce-cost-optimization-advisor`	Cost optimization recommendations
`huawei-cloud-cce-observability-context-builder`	Observability context enrichment

Capabilities

Collect node metrics, HPA status, nodepool autoscaling status, and business Deployment inventory
Calculate trend statistics (avg, min, max, p95, slope, trend direction) for CPU, memory, and disk
Predict bottleneck arrival time against configurable thresholds
Simulate recommended node counts and reducible nodes with headroom
Generate JSON/Markdown/HTML reports with embedded SVG curve charts
Store and compare recurring history records to track before-and-after effects
Preview and apply HPA configuration with two-step confirmation workflow
Recommend node autoscaler min/max bounds adjustments

Typical Use Cases

Run a 6-hour capacity trend analysis and identify rising CPU trends approaching bottleneck threshold
Compare weekly capacity records to validate that a previous optimization reduced utilization
Simulate node requirements with 60% CPU target and 15% headroom, then preview HPA adjustments
Forecast when memory will reach 80% bottleneck threshold based on current slope
Generate a monthly capacity report with curve charts for stakeholder review
Check HPA coverage percentage and preview HPA manifest for uncovered Deployments

Prerequisites

Python Dependencies

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

huaweicloudsdkcore
huaweicloudsdkaom
huaweicloudsdkcce
huaweicloudsdkiam

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.

Security rules for credentials:

No persistent storage — never write AK/SK, tokens, or certificates to disk files
No long-term memory cache — AK/SK exists only during the current API call and is released afterward
Project ID memory cache only — only non-sensitive project IDs may be cached in process memory (never written to disk)
No log leakage — never include AK/SK in logs, response output, or error messages
Output desensitization — output only capacity, trend, and simulation information; never expose authentication credentials

AK/SK may be provided in two ways:

Via environment variables HUAWEI_AK / HUAWEI_SK (recommended)
Via per-call parameters ak and sk (not recommended for production)

IAM Permissions

Permission	Description
`cce:cluster:list`	Query CCE cluster metadata
`cce:node:list`	Query Kubernetes node inventory
`cce:nodepool:list`	Query CCE nodepool autoscaling status
`cce:deployment:list`	Query business Deployment inventory
`aom:metric:get`	Query AOM Prometheus metrics (CPU, memory, disk time series)
`cce:hpa:list`	Query HPA inventory and target references
`cce:hpa:configure`	Configure HPA (preview + confirm workflow)

Core Tools

All actions are invoked via the dispatcher script:

bash

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

Two-Step Confirmation Workflow for Mutation Operations

Mutation operations (apply HPA configuration with confirm=true, change nodepool autoscaling bounds, resize node pools) require confirm=true to execute. Without confirm, the tool returns a preview and confirmation prompt only.

Step 1: Preview — call without confirm:

bash

python3 scripts/huawei-cloud.py huawei_configure_cce_hpa \
  region=cn-north-4 cluster_id=<cluster-id> \
  namespace=default deployment_name=<name> \
  min_replicas=2 max_replicas=10 target_cpu_percent=60

Returns: operation preview, target HPA, configuration fields, and confirmation example. No real modification is performed.

Step 2: Confirm execution — call again with confirm=true:

bash

python3 scripts/huawei-cloud.py huawei_configure_cce_hpa \
  region=cn-north-4 cluster_id=<cluster-id> \
  namespace=default deployment_name=<name> \
  min_replicas=2 max_replicas=10 target_cpu_percent=60 \
  confirm=true

Operations Requiring Confirmation

Tool	Operation	Risk Level	Description
`huawei_configure_cce_hpa`	Configure HPA	🟠 High	Apply HPA configuration changes; preview without `confirm`, apply with `confirm=true`
Nodepool autoscaling changes	Modify bounds	🟠 High	Change min/max nodes in nodepool autoscaler configuration
Node pool resize	Resize pool	🔴 High	Add or remove nodes from a node pool

Prohibited Actions

Action	Description
Automatic downsizing on single period	Do not treat a single period as enough evidence for aggressive downsizing; prefer at least two comparable records
Apply HPA without preview	Always preview first, then confirm only after explicit user approval
Skip rollback plan	For real changes, provide the exact reason, expected effect, rollback path, and validation checks
Modify workloads directly	Do not mutate Deployment replicas, node pools, or addons without explicit authorization

Capacity Trend Analysis (Read-Only)

Action	Description	Required Params
`huawei_analyze_cce_capacity_trend`	Main analyzer: collect metrics, compute trends, simulate elasticity, generate reports with curve charts, store history records	`region`, `cluster_id`

bash

# Basic 6-hour analysis
python3 scripts/huawei-cloud.py huawei_analyze_cce_capacity_trend \
  region=cn-north-4 cluster_id=<cluster-id> hours=6

# Weekly analysis with custom targets and output directory
python3 scripts/huawei-cloud.py huawei_analyze_cce_capacity_trend \
  region=cn-north-4 cluster_id=<cluster-id> hours=168 \
  target_cpu_percent=60 target_memory_percent=70 \
  headroom_percent=15 bottleneck_percent=80 \
  output_dir=debug/capacity-trend/<cluster>

# Monthly analysis with business namespace scope and action note
python3 scripts/huawei-cloud.py huawei_analyze_cce_capacity_trend \
  region=cn-north-4 cluster_id=<cluster-id> hours=744 \
  business_namespaces=default,production \
  action_note="Scaled HPA max_replicas from 5 to 10 on 2026-05-28"

# Recurring run for comparison
python3 scripts/huawei-cloud.py huawei_analyze_cce_capacity_trend \
  region=cn-north-4 cluster_id=<cluster-id> hours=6 \
  output_dir=debug/capacity-trend/<cluster> \
  history_dir=debug/capacity-trend/<cluster>/history

Inventory and Context (Read-Only)

Action	Description	Required Params
`huawei_list_cce_clusters`	List CCE clusters in a region	`region`
`huawei_get_kubernetes_nodes`	Get Kubernetes node inventory for a cluster	`region`, `cluster_id`
`huawei_list_cce_nodepools`	List CCE nodepools with autoscaling status	`region`, `cluster_id`
`huawei_get_cce_deployments`	Get business Deployment inventory	`region`, `cluster_id`
`huawei_list_cce_hpas`	List HPA inventory and target references	`region`, `cluster_id`

bash

# List clusters
python3 scripts/huawei-cloud.py huawei_list_cce_clusters region=cn-north-4

# Get node inventory
python3 scripts/huawei-cloud.py huawei_get_kubernetes_nodes \
  region=cn-north-4 cluster_id=<cluster-id>

# List nodepools
python3 scripts/huawei-cloud.py huawei_list_cce_nodepools \
  region=cn-north-4 cluster_id=<cluster-id>

# Get deployments
python3 scripts/huawei-cloud.py huawei_get_cce_deployments \
  region=cn-north-4 cluster_id=<cluster-id>

# List HPA configurations
python3 scripts/huawei-cloud.py huawei_list_cce_hpas \
  region=cn-north-4 cluster_id=<cluster-id>

Metrics (Read-Only)

Action	Description	Required Params
`huawei_get_cce_node_metrics_topN`	Get Node CPU/memory/disk TopN	`region`, `cluster_id`
`huawei_get_aom_metrics`	Get AOM Prometheus metric time series	`region`, `cluster_id`

bash

# Node metrics TopN
python3 scripts/huawei-cloud.py huawei_get_cce_node_metrics_topN \
  region=cn-north-4 cluster_id=<cluster-id> top_n=10 hours=6

# AOM metrics
python3 scripts/huawei-cloud.py huawei_get_aom_metrics \
  region=cn-north-4 cluster_id=<cluster-id>

HPA Configuration (Preview + Confirm)

Action	Description	Risk Level	Requires `confirm`	Required Params
`huawei_generate_cce_hpa_manifest`	Generate HPA manifest preview	🟢 Low	No	`region`, `cluster_id`, `namespace`, `deployment_name`, `min_replicas`, `max_replicas`, `target_cpu_percent`
`huawei_configure_cce_hpa`	Preview or apply HPA configuration	🟠 High	Yes for apply	`region`, `cluster_id`, `namespace`, `deployment_name`, `min_replicas`, `max_replicas`, `target_cpu_percent`

bash

# Generate HPA manifest preview (read-only)
python3 scripts/huawei-cloud.py huawei_generate_cce_hpa_manifest \
  region=cn-north-4 cluster_id=<cluster-id> \
  namespace=default deployment_name=<name> \
  min_replicas=2 max_replicas=10 target_cpu_percent=60

# Preview HPA configuration (no execution)
python3 scripts/huawei-cloud.py huawei_configure_cce_hpa \
  region=cn-north-4 cluster_id=<cluster-id> \
  namespace=default deployment_name=<name> \
  min_replicas=2 max_replicas=10 target_cpu_percent=60

# Confirm and apply HPA configuration
python3 scripts/huawei-cloud.py huawei_configure_cce_hpa \
  region=cn-north-4 cluster_id=<cluster-id> \
  namespace=default deployment_name=<name> \
  min_replicas=2 max_replicas=10 target_cpu_percent=60 \
  confirm=true

Parameter Reference

Capacity Trend Analysis Parameters

Parameter	Required	Description
`region`	Yes	Huawei Cloud region (e.g., `cn-north-4`)
`cluster_id`	Yes	CCE cluster ID
`hours`	No	Analysis window in hours, clamped to 1-744 (default: 6)
`output_dir`	No	Directory to write reports, charts, and history records
`history_dir`	No	Override the default history folder for recurring comparison
`business_namespaces`	No	Comma-separated namespace allowlist for business Deployments
`exclude_namespaces`	No	Comma-separated namespace exclude list (default: `kube-system`)
`target_cpu_percent`	No	Simulation CPU target utilization (default: 60)
`target_memory_percent`	No	Simulation memory target utilization (default: 70)
`headroom_percent`	No	Simulation headroom percentage (default: 15)
`bottleneck_percent`	No	Bottleneck threshold percentage (default: 80)
`action_note`	No	Record an optimization action or operational event for before-and-after comparison
`ak`	No	Access Key ID; `HUAWEI_AK` environment variable preferred
`sk`	No	Secret Access Key; `HUAWEI_SK` environment variable preferred
`project_id`	No	Huawei Cloud project ID; auto-obtained via IAM API when not provided

HPA Configuration Parameters

Parameter	Required	Description
`region`	Yes	Huawei Cloud region
`cluster_id`	Yes	CCE cluster ID
`namespace`	Yes	Kubernetes namespace of the target Deployment
`deployment_name`	Yes	Target Deployment name
`min_replicas`	Yes	HPA minimum replica count
`max_replicas`	Yes	HPA maximum replica count
`target_cpu_percent`	No	HPA target CPU utilization percentage (default: 60)
`target_memory_percent`	No	HPA target memory utilization percentage
`confirm`	No	Must be explicitly set to `true` to apply HPA configuration; without it, returns preview only

Output Format

Capacity Trend Report

Each analysis run writes the following files:

File	Description
`capacity-trend-summary.json`	Full structured JSON output (see `references/output-schema.md`)
`capacity-trend-report.md`	Markdown report with trend statistics and recommendations
`capacity-trend-report.html`	HTML report with embedded SVG curve charts
`capacity-trend-chart.svg`	CPU/memory/disk trend curve chart
`capacity-simulation-chart.svg`	Simulation result curve chart
`history/capacity-trend-*.json`	Individual history record for recurring comparison
`capacity-trend-history.jsonl`	Append-only history log for cross-run comparison

Key Output Fields

Field	Description
`capacity_stats`	Per-resource trend statistics: avg, min, max, p95, latest, slope, trend direction, bottleneck prediction
`elasticity`	HPA coverage percentage, node autoscaler enabled status with min/max/current nodes
`simulation`	Recommended node counts, reducible nodes, capped sample percentage
`recommendations`	Prioritized optimization suggestions with area, reason, and configuration methods
`history_comparison`	Deltas between current and previous record when history is available
`data_gaps`	Missing metric data that prevents reliable trend conclusions

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

Verification

Run the dispatcher with a known region and cluster to confirm connectivity:

bash

python3 scripts/huawei-cloud.py huawei_analyze_cce_capacity_trend \
  region=cn-north-4 cluster_id=<cluster-id> hours=1

Verify that capacity_stats includes trend direction and bottleneck prediction for CPU, memory, and disk
Verify that simulation.avg_recommended_nodes and max_recommended_nodes are within autoscaler min/max bounds
Verify that capacity-trend-report.html contains embedded SVG charts
Run a second analysis and verify that history_comparison shows deltas from the first run
Test HPA preview workflow: call huawei_configure_cce_hpa without confirm and verify it returns a preview only
After a confirmed HPA change, run a new capacity analysis to compare before-and-after effects

Best Practices

Always use huawei_analyze_cce_capacity_trend as the main action; it collects, analyzes, simulates, and generates reports in a single call
Accept windows from 1 hour to 1 month; common schedules are every 6 hours, daily, weekly, and monthly
Do not treat a single period as enough evidence for aggressive downsizing; prefer at least two comparable records before lowering baseline capacity
If the simulation recommends lower capacity but p95 or max utilization is close to the bottleneck threshold, keep headroom and propose observation instead
For real changes, provide the exact reason, expected effect, rollback path, and validation checks
Configuration execution must be user-approved and should be followed by a new capacity record to compare before-and-after effects
Use action_note to record optimization actions so the next cycle can compare their effects
If AOM metrics are unavailable, report the gap and do not invent trend conclusions
Always consider both workload elasticity (HPA) and node elasticity (nodepool autoscaling) when making recommendations

Reference Documents

Document	Description
`references/workflow.md`	Capacity trend workflow: scope, collection, trend analysis, elasticity awareness, recurring comparison, and output order
`references/simulation-rules.md`	Simulation rules: targets, node simulation formula, HPA advice, node autoscaler advice, and execution boundaries
`references/output-schema.md`	Output JSON schema for capacity trend analysis results
Huawei Cloud Python SDK Documentation	SDK reference
Huawei Cloud API Explorer	API interactive explorer

Notes

This skill has read-only tools (analysis, simulation, report generation) and preview tools for HPA configuration — applying configuration changes requires confirm=true two-step confirmation
Do not treat a single period as enough evidence for aggressive downsizing; prefer at least two comparable records
If simulation recommends lower capacity but p95 is near bottleneck threshold, keep headroom and propose observation
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
Use action_note to record optimization events so subsequent runs can validate their effects
If remediation actions are needed (scale, reboot, drain), output recommendations only and hand off to huawei-cloud-cce-auto-remediation-runner

Common Pitfalls

Pitfall	Correct Approach
Treating a single period as enough evidence for downsizing	Prefer at least two comparable records before lowering baseline capacity
Recommending lower capacity when p95 is near bottleneck threshold	Keep headroom and propose observation; low average does not override high p95
Applying HPA configuration without preview step	Always call `huawei_configure_cce_hpa` without `confirm` first to preview; only add `confirm=true` after explicit user approval
Inventing trend conclusions when AOM metrics are unavailable	Report the data gap explicitly; do not fabricate trend analysis from incomplete data
Ignoring node elasticity when making workload elasticity recommendations	Always consider both HPA coverage and nodepool autoscaling together; workload scaling is constrained by node capacity
Not running a follow-up analysis after configuration changes	After any confirmed change, run a new capacity analysis and compare before-and-after effects using `action_note`
Using default simulation targets for latency-sensitive or bursty systems	Lower CPU/memory targets or increase headroom for systems with burst patterns or strict latency requirements
Skipping history comparison for recurring runs	Always compare the latest record with previous records to track utilization deltas and validate recommendations