Huawei Cloud — CCE ENV Assessment Skill

Overview

Prerequisites

Prerequisite check: Huawei Cloud CLI (hcloud) >= 7.2.2 required Run

hcloud version

to verify the version is >= 7.2.2, and

hcloud configure list

to confirm a profile exists. If it is not installed or the version is too low, see references/koocli-installation-guide.md for the installation guide.

hcloud version
hcloud configure list

Prerequisite check: Python >= 3.6 required Run

python --version

to verify the version is >= 3.6.0 If it is not installed or the version is too low, The skill execution is interrupted proactively, and the user is prompted to install Python 3.6 or later. If the Python version is 3.6.x, run the following command to upgrade pip to the latest version

pip3 install --user --upgrade 'pip<22'

⚠️ Mandatory Execution Rules

Rule 1: Step-by-step Confirmation

1. Print the completion status of the current step
2. Show the output produced by that step
3. Every Skill run starts from Step 1 — do NOT skip Step 1 and jump into later steps
4. Strictly follow this Skill's rules and wait for user confirmation after every step
5. Strictly follow the described flow; do not perform extra operations. If anything errors, return the error to the user as-is — do not try to fix it yourself
6. All execution rules take precedence over efficiency

Rule 2: Fixed Directories

1. Save all intermediate files under the

   data/

   directory

Rule 3: Permission Issues

1. When you hit a permission problem, try

   sudo

   . If that does not resolve it, error out and abort the Skill flow

Rule 4: Anti-skip Check

data/

artifacts/

data/cloud-native-collection.md

artifacts/cloud-native-summary.xlsx

Rule 5: Information Collection Method

1. For every piece of information collected, prefer the Huawei Cloud KooCLI tool (

   hcloud

   commands)

Rule 6: Status Tracking

📋 Cloud-Native Container Assessment — Status Board
├────────────────────────────────────────────┤
│ Step 1: Configuration       [⏳ In progress]│
│ Step 2: Environment check   [○ Pending]     │
│ Step 3: Information collect [○ Pending]     │
│ Step 4: Metric scoring      [○ Pending]     │
│ Step 5: Report generation   [○ Pending]     │
└────────────────────────────────────────────┘

📋 Cloud-Native Container Assessment — Status Board
├────────────────────────────────────────────┤
│ Step 1: Configuration       [✅ Done]       │
│ Step 2: Environment check   [✅ Done]       │
│ Step 3: Information collect [✅ Done]       │
│ Step 4: Metric scoring      [✅ Done]       │
│ Step 5: Report generation   [⏳ In progress]│
└────────────────────────────────────────────┘

🔐 Permission Boundary

The AI is allowed to:

• Follow the SKILL steps exactly
• Invoke only the scripts specified in the SKILL
• Return errors as-is when problems occur

The AI is forbidden to:

• Temporarily modify or bypass the workflow
• Switch to an alternative approach (e.g. fall back to Markdown when html fails)
• Modify any script or configuration without user consent
• Perform any operation not explicitly listed in the SKILL

Handling out-of-bounds operations

Core Workflows

Step 1: Configuration

HWC_AK

HWC_SK

CCE_Region

CCE_NAME

Dockerfile_REPO_URL

Step 2: Environment Check

1. Use

   hcloud

   with AK/SK to access the Huawei Cloud CCE environment and confirm it is reachable, If it is not installed or the version is too low, see references/koocli-installation-guide.md for the installation guide.
2. Verify that the local Python environment is working correctly, Python version requirement is greater than 3.6, If the Python version is 3.6.x, run the following command to upgrade pip to the latest version

   pip3 install --user --upgrade 'pip<22'

3. Check whether the Python dependency library is installed. Go to the references directory. If the Python version is 3.6.x, run the command

   python3 -m pip install -r requirements.txt

   . If the Python version is later than 3.7, Execute commands using a Python virtual environment

   pip3 install -r requirements.txt

   .
4. Empty any historical files inside

   data/

   ; if

   data/

   does not exist, create it
5. Empty any historical files inside

   artifacts/

   ; if

   artifacts/

   does not exist, create it
6. Once the checks complete, return the result to the user and wait for confirmation on whether to install dependencies or to continue

Step 3: Container Environment Information Collection

references/cloud-native-checklist.xlsx

1. Every collection run MUST be driven by the actual metric items in

   references/cloud-native-checklist.xlsx

   and MUST invoke

   scripts/collect_all.py

   to collect fresh data — do NOT reuse historical or cached data
2. Acceptance metric: the metric item being assessed
3. Quantified target: the reference standard for that metric
4. Acceptance method: how the environment information for that metric is collected
5. During collection, if any required piece of information cannot be obtained, explore alternative ways to retrieve it and ask the user for confirmation
6. Once collection is complete for every metric, fill the collection method (including, but not limited to, Python scripts and executed commands) and the collected data into

   templates/cloud-native-assessment-template.md

   , write the result to

   data/cloud-native-collection.md

   , and show it to the user

Step 4: Metric Scoring

data/cloud-native-collection.md

1. Compare the collected data against the quantified target to derive an acceptance verdict (Fully Satisfied / Mostly Satisfied / Partially Satisfied / Not Satisfied; metrics that cannot be compared are recorded as Not Satisfied; "Not Applicable" and "Not Evaluated" both roll up into Not Satisfied, and the basis field must state that the metric is N/A or Not Evaluated) and a scoring basis (describe based on the collected data — include the metric's environment information, the collection method used, and the reason for non-satisfaction)
2. Convert the verdict to a score: Fully Satisfied = 3, Mostly Satisfied = 2, Partially Satisfied = 1, Not Satisfied = 0
3. Invoke

   scripts/score_and_excel.py

   to produce a fresh scoring sheet, output as Excel containing the columns: number, cloud-native dimension, level, acceptance metric, quantified target, acceptance method, description, acceptance verdict, score, full score, scoring basis. Save to

   artifacts/cloud-native-summary.xlsx

Step 5: Report Generation

artifacts/cloud-native-summary.xlsx

1. The final report uses

   templates/report_template.md

   as its base template
2. The report title is fixed as "Cloud-Native Assessment Report"
3. The report contains 4 chapters; Chapter 1 and Chapter 2 reuse the content of

   templates/report_template.md

   directly
4. Chapter 3 of the final report is populated from the scoring sheet

   artifacts/cloud-native-summary.xlsx

5. Invoke

   scripts/make_charts.py

   against the

   cloud-native-summary.xlsx

   produced in Step 4 to generate a radar chart and a staircase chart
   Chart generation requirements:
   • Radar chart: group metrics by cloud-native dimension. The score for each dimension = (sum of that dimension's metric scores / sum of that dimension's full scores) × 5. The radar has exactly six dimensions: Service-orientation, Security, Automation, Elasticity, Observability, Resilience. If a metric belongs to multiple dimensions (e.g. metric #1 belongs to both Service-orientation and Automation), it contributes to the scoring of every dimension it belongs to.
   • Staircase chart: overall score = (sum of all metric scores / sum of all full scores) × 5. Stage thresholds — score ≤ 1: Traditional; 1 < score ≤ 2: Basic Cloud; 2 < score ≤ 3: Service-Oriented; 3 < score ≤ 4: Automated; score > 4: Intelligent. The chart must visually render as stairs.
6. Place the generated radar chart into report section 3.3.3 and the staircase chart into 3.3.4
7. Replace Chapter 4 of the final report with the actual remediation recommendations
8. Based on the assessment results, output remediation recommendations by P0/P1/P2 priority:
   • P0: Not Satisfied (0 pts) — mandatory items
   • P1: Partially Satisfied (1 pt) — items to fix
   • P2: Mostly Satisfied (2 pts) — recommended improvements
9. Invoke

   scripts/make_report_html.py

   to generate the final report as a html, written to

   artifacts/cloud-native-report.html

Core Commands

Get cluster ID and basic info

hcloud cce ListClusters --cli-region=cn-north-4

Get cluster kubeconfig certificate

hcloud cce CreateKubernetesClusterCert \
  --cli-region=cn-north-4 \
  --cluster_id=a5659ec8-55b5........ \
  --duration=1

Get detailed cluster info

hcloud cce ListClusters --cli-region=cn-north-4

Authentication parameters

--cli-access-key

--cli-secret-key

--cli-security-token

--cli-profile

--cli-mode

--cli-region

Output Format

1. The final report is delivered as a html file
2. The scoring sheet is delivered as an Excel file
3. All output files are saved under the

   artifacts/

   directory

Verification

Post-install verification

1. Version check:

   hcloud version

   MUST return 7.2.2 or higher
2. Help check:

   hcloud --help

   MUST list the available services

Authentication verification

1. Profile check:

   hcloud configure list

   MUST show the configured profile

Best Practices

1. Evaluate and analyze the container environment running on Huawei Cloud.
2. Harden the configuration of existing container clusters and modify the cluster configuration based on the optimization suggestions.

References

• report_template.md

  — Final report template

• cloud-native-checklist.xlsx

  — Description of assessment metrics

• requirements.txt

  — Python dependency library

Scripts

• collect_all.py

  — Main assessment script; integrates all collection modules

• make_charts.py

  — Chart generation script

• make_report.py

  — Report generation script

• score_and_excel.py

  — Scoring sheet generation script

Templates

• report_template.md

  — Final report template

• cloud-native-assessment-template.md

  — Data collection template

Notes

1. Make sure the Huawei Cloud container environment is reachable
2. Make sure the Huawei Cloud credentials are valid and not expired
3. If the Huawei Cloud Koocli tool and Python environment cannot be automatically installed using the skill, it is recommended that users install them manually.
4. Currently only public-network access to the container environment is supported
5. Output may be truncated during execution; clearing historical data and re-running the Skill is recommended in that case