Alibabacloud Governance Evaluation Report
by @sdk-team
Alibaba Cloud Governance Center evaluation report skill. Use for querying governance maturity check results, generating structured risk reports, and account...
clawhub install alibabacloud-governance-evaluation-report📖 About This Skill
name: alibabacloud-governance-evaluation-report description: | Alibaba Cloud Governance Center evaluation report skill. Use for querying governance maturity check results, generating structured risk reports, and account compliance analysis. Triggers: "云治理", "成熟度检测", "合规检查", "安全风险", "治理检测", "governance evaluation", "maturity check", "compliance report", "risk report", "governance center".
Alibaba Cloud Governance Center Evaluation Report
Guide users to discover governance risks, focus on critical issues, and take remediation actions through a progressive drill-down workflow.
Scenario Description
This skill is a problem-discovery and resolution guide — not a comprehensive audit report generator. It operates as a progressive disclosure funnel:
1. Overview (quick diagnosis) — Score + pillar distribution + top critical risks → guide user to choose a direction 2. Pillar analysis (focused drill-down) — All risks in a specific domain, controlled by severity → guide user to specific items 3. Detail (deep dive) — Single check item with full remediation steps → guide user to related items or resources 4. Resources (action) — Non-compliant resource listing for targeted remediation
Each layer focuses on the most important information and guides the user to the next level. Avoid information overload — keep output concise and actionable.
Architecture: Governance Center API → CLI (aliyun governance) → governance_query.py (merge + cache) → JSON output → Agent report
How It Works
Data Sources — Three APIs provide all data:
1. list-evaluation-metadata — Check item definitions (name, description, pillar, level, remediation)
2. list-evaluation-results — Actual results (status, risk, compliance rate, score)
3. list-evaluation-metric-details — Non-compliant resource details for a specific check item
Processing — The script (governance_query.py) merges data sources and caches results for 1 hour. It provides 4 query modes: overview, pillar, detail, resources.
Output — Structured JSON for Agent to generate user-friendly reports. Reports are output directly in the conversation as formatted text, NOT written to files.
Prerequisites
> Pre-check: Aliyun CLI >= 3.3.0 required
> Run aliyun version to verify. If not installed or version too low,
> see references/cli-installation-guide.md for installation instructions.
> Then [MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.
aliyun version # >= 3.3.0
aliyun configure set --auto-plugin-install true # Enable auto plugin install
python3 --version # Python 3.x
Authentication
Configure CLI authentication (OAuth recommended):
# OAuth mode (recommended)
aliyun configure --mode OAuthRAM Policy
Requires Governance Center read permissions. See references/ram-policies.md for full policy.
Minimum required permissions:
governance:ListEvaluationMetadata
governance:ListEvaluationResultsOr attach system policy: AliyunGovernanceReadOnlyAccess
Parameter Confirmation
This skill has minimal user-specific parameters. The following may require confirmation:
| Parameter Name | Required/Optional | Description | Default Value |
|----------------|-------------------|-------------|---------------|
| --profile | Optional | Aliyun CLI profile name | Default profile |
| -c, --category | Required (pillar mode) | Pillar category name | N/A |
| --id | Required (detail/resources mode) | Check item metric ID | N/A |
| --keyword | Optional (detail mode) | Search keyword for check items | N/A |
| --max-results | Optional (resources mode) | Max results per page | 50 |
Verification
Verify setup before use:
bash
Test CLI connection
aliyun governance list-evaluation-results \ --user-agent AlibabaCloud-Agent-Skills \ --cli-query "Results.TotalScore"Test script
python3 scripts/governance_query.py overview
See references/verification-method.md for detailed steps.
Core Workflow
> IMPORTANT: Parameter Confirmation — Before executing any command or API call,
> ALL user-customizable parameters (e.g., --profile, --category, --id, --keyword,
> --max-results, etc.) MUST be confirmed with the user.
> Do NOT assume or use default values without explicit user approval.
> IMPORTANT: Output Format — Reports are format specifications for conversation output only.
> Always output report content directly in the chat message as formatted Markdown.
> Do NOT create or write report files (e.g., .md, .txt, .html). No file generation is needed.
Script location: scripts/governance_query.py
Global Options
| Option | Description |
|--------|-------------|
| --refresh | Force refresh cache (default: 1-hour TTL) |
Mode 1: overview — Overall Maturity Report
When to use: User asks about overall account health, maturity score, or wants a summary.
bash
python3 scripts/governance_query.py overview
python3 scripts/governance_query.py overview -r Error # Only high-risk items
python3 scripts/governance_query.py overview -r Error,Warning # High + medium risk
python3 scripts/governance_query.py --refresh overview # Force fresh data
Options:| Option | Description |
|--------|-------------|
| -r, --risk | Filter RiskyItems by risk level (comma-separated: Error, Warning, Suggestion). PillarSummary and RiskDistribution are always complete. |
Output JSON fields:
TotalScore — Overall maturity score (0.0-1.0)
PillarSummary — Per-pillar statistics (checked/risky counts, always unfiltered)
RiskDistribution — Count by risk level (always unfiltered)
RiskyItems — Items with risk, filtered by --risk if specified, sorted by severity
RiskFilter — Applied risk filter values (only present when --risk is used)Report format: Read references/report-format-overview.md for the exact output format.
Mode 2: pillar — Pillar-Specific Report
When to use: User asks about a specific domain (security, reliability, cost, etc.).
bash
python3 scripts/governance_query.py pillar -c
Options:| Option | Description |
|--------|-------------|
| -c, --category | Required. Pillar name (see below) |
| --risky | Only show items with risk (exclude compliant) |
| -l, --level | Filter by recommendation level (comma-separated) |
| -r, --risk | Filter by actual risk level (comma-separated) |
Category values:
Security — 安全
Reliability — 稳定
CostOptimization — 成本
OperationalExcellence — 效率
Performance — 性能Level values: Critical, High, Medium, Suggestion
Risk values: Error, Warning, Suggestion, None
Examples:
bash
安全支柱所有风险项
python3 scripts/governance_query.py pillar -c Security --risky仅严重和高优先级的错误/警告
python3 scripts/governance_query.py pillar -c Security -l Critical,High -r Error,Warning --risky
Output JSON fields:
Category, CategoryCN — Pillar name
MatchedCount — Number of matched items
Items — List of check items with statusReport format: Read references/report-format-pillar.md for the exact output format.
Mode 3: detail — Check Item Detail
When to use: User asks about a specific check item or how to fix an issue.
bash
python3 scripts/governance_query.py detail --id
Options:| Option | Description |
|--------|-------------|
| --id | Check item ID (e.g., apbxftkv5c) |
| --keyword | Search by name/description (if multiple matches, shows list) |
Examples:
bash
按 ID 查询
python3 scripts/governance_query.py detail --id apbxftkv5c按关键字搜索
python3 scripts/governance_query.py detail --keyword "MFA"
Output JSON fields:
Basic info: Id, DisplayName, Description, Category
Status: Status, Risk, Compliance, NonCompliant
Remediation — Fix steps (Manual/Analysis/QuickFix)Report format: Read references/report-format-detail.md for the exact output format. The detail format also covers the resources listing when needed.
Mode 4: resources — Non-Compliant Resources
When to use: User wants to see which specific resources failed a check item.
bash
python3 scripts/governance_query.py resources --id
Options:| Option | Description |
|--------|-------------|
| --id | Required. Check item ID |
| --max-results | Max results per page (default: 50) |
Examples:
bash
查询未启用 MFA 的 RAM 用户列表
python3 scripts/governance_query.py resources --id apbxftkv5c查询开放高危端口的安全组
python3 scripts/governance_query.py resources --id a9g6pv7r5b
Output JSON fields:
MetricId — Check item ID
TotalCount — Number of non-compliant resources
Resources[] — List of resources:
- ResourceId, ResourceName, ResourceType
- RegionId, ResourceOwnerId
- Classification — Risk classification
- Properties — Resource-specific attributes
Mode Selection Guide
| User says... | Use mode | Command | Report format |
|--------------|----------|---------|---------------|
| "查查我的账号安全吗" / "成熟度得分" / "分析下治理检测结果" | overview | overview | overview |
| "有哪些高风险项" / "看下所有高风险" | overview | overview -r Error | overview |
| "中风险以上的问题" | overview | overview -r Error,Warning | overview |
| "安全方面有哪些问题" / "XX支柱的风险" | pillar | pillar -c Security --risky | pillar |
| "网络安全相关的检测项" / "数据库风险" | pillar + keyword filter | pillar -c Security --risky then filter by keyword | pillar |
| "高优先级的问题" | pillar | pillar -c Security -l Critical,High --risky | pillar |
| "MFA怎么修" / "XX检测项详情" | detail | detail --keyword "MFA" | detail |
| "哪些用户没开MFA" / "不合规资源有哪些" | detail + resources | detail --id xxx then resources --id xxx | detail |
Default: If user doesn't specify pillar or check item, use overview.
Report format selection: After determining the query mode, read the corresponding report format reference file before generating output. Only read the format file that matches the user's intent — do not read all format files at once.
Field Reference
| Field | Values | Note |
|-------|--------|------|
| Risk | Error(高风险) > Warning(中风险) > Suggestion(低风险) > None(合规) | Actual detected risk |
| RecommendationLevel | Critical > High > Medium > Suggestion | Recommended priority |
| Status | Finished / NotApplicable / Failed | Check execution status |
| Compliance | 0.0 - 1.0 | 1.0 = fully compliant |
Cache & Cleanup
Only metadata (check item definitions) is cached locally — results are always fetched in real-time.
Cache location: ~/.governance_cache/metadata.json
TTL: 24 hours (metadata rarely changes)
list-evaluation-results and list-evaluation-metric-details are never cachedbash
Force refresh metadata cache
python3 scripts/governance_query.py --refresh overviewClear cache manually
rm -rf ~/.governance_cache/ ``Best Practices
1. Focus, don't dump — Each report layer should highlight what matters most, not list everything. Read the corresponding report format reference for quantity control rules
2. Follow the funnel — Start with
overview, guide user to pillar, then to detail. Don't skip layers unless user explicitly asks for a specific item
3. Use --risky filter for pillar mode — Reduces noise by hiding compliant items when investigating issues
4. Prioritize by Risk + Level — Focus on Error risk with Critical/High recommendation level first
5. Follow remediation guidance — Use detail mode to get actionable fix steps before modifying resources
6. Always guide next steps — Every report must end with follow-up guidance based on actual data, helping users continue exploring
7. Cache management — Only metadata is cached (24h TTL); results are always real-time. Use --refresh` to force metadata refreshReferences
| File | Content | |------|---------| | report-format-overview.md | Report format: overall governance overview | | report-format-pillar.md | Report format: pillar / keyword aggregated analysis | | report-format-detail.md | Report format: single check item detail + resources | | related-apis.md | CLI commands and API details | | ram-policies.md | Required permissions | | verification-method.md | Verification steps | | cli-installation-guide.md | CLI installation |
⚙️ Configuration
> Pre-check: Aliyun CLI >= 3.3.0 required
> Run aliyun version to verify. If not installed or version too low,
> see references/cli-installation-guide.md for installation instructions.
> Then [MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.
aliyun version # >= 3.3.0
aliyun configure set --auto-plugin-install true # Enable auto plugin install
python3 --version # Python 3.x
📋 Tips & Best Practices
1. Focus, don't dump — Each report layer should highlight what matters most, not list everything. Read the corresponding report format reference for quantity control rules
2. Follow the funnel — Start with overview, guide user to pillar, then to detail. Don't skip layers unless user explicitly asks for a specific item
3. Use --risky filter for pillar mode — Reduces noise by hiding compliant items when investigating issues
4. Prioritize by Risk + Level — Focus on Error risk with Critical/High recommendation level first
5. Follow remediation guidance — Use detail mode to get actionable fix steps before modifying resources
6. Always guide next steps — Every report must end with follow-up guidance based on actual data, helping users continue exploring
7. Cache management — Only metadata is cached (24h TTL); results are always real-time. Use --refresh to force metadata refresh