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 >= 3.3.3 required Run aliyun version to verify >= 3.3.3. If not installed or version too low, use a package manager or the pinned, SHA256-verified packages in references/cli-installation-guide.md. Do not pipe a remotely downloaded installation script directly into a shell.

Then [MUST] run aliyun plugin update to ensure that any existing plugins on your local machine are always up-to-date.

[MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.

aliyun version                                    # >= 3.3.3
aliyun configure set --auto-plugin-install true   # Enable auto plugin install
python3 --version                                 # Python 3.x

Observability

Every Alibaba Cloud API invocation made by this skill must include the complete User-Agent template:

--user-agent AlibabaCloud-Agent-Skills/{SKILL_NAME}/{session-id}

For this skill, {SKILL_NAME} is alibabacloud-governance-evaluation-report, so the concrete form is:

--user-agent AlibabaCloud-Agent-Skills/alibabacloud-governance-evaluation-report/{session-id}

Session ID rules:

1. Generate a new session ID once per session (one skill invocation) with uuid.uuid4().hex. It must be a 32-char hex UUID v4 value: exactly 32 lowercase hexadecimal characters with no hyphens.
2. Reuse the same session ID for every Alibaba Cloud API call in that invocation, including direct CLI commands, helper-script calls, pagination, and retries.
3. Do not regenerate the session ID between related calls. Generate a new session ID only when a new skill invocation begins.
4. Set ALIBABA_CLOUD_AGENT_SESSION_ID to that 32-char hex session ID before running governance_query.py. The script validates and reuses the supplied UUID; when the variable is absent, it generates one 32-char hex UUID v4 per process and reuses it.
5. Never derive a session ID from account IDs, credentials, user data, or other sensitive values.

Before running governance_query.py, tell the user that it invokes the local aliyun executable with their current CLI credentials and sends read-only queries to Alibaba Cloud Governance Center. The script enforces a read-only command allowlist, validates all dynamic arguments, and prints the resolved executable and API action to stderr before each call.

Authentication

Configure CLI authentication (OAuth recommended):

# OAuth mode (recommended)
aliyun configure --mode OAuth

RAM Policy

Requires Governance Center read permissions. See references/ram-policies.md for full policy.

Minimum required permissions:

• governance:ListEvaluationMetadata
• governance:ListEvaluationResults

Or attach system policy: AliyunGovernanceReadOnlyAccess

Parameter Confirmation

This skill has minimal user-specific parameters. The following may require confirmation:

Verification

Verify setup before use:

# Test CLI connection
aliyun governance list-evaluation-results \
  --user-agent AlibabaCloud-Agent-Skills/alibabacloud-governance-evaluation-report/{session-id} \
  --cli-query "Results.TotalScore"

# Test script
export ALIBABA_CLOUD_AGENT_SESSION_ID="{session-id}"
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

Mode 1: overview — Overall Maturity Report

When to use: User asks about overall account health, maturity score, or wants a summary.

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:

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.).

python3 scripts/governance_query.py pillar -c <Category> [options]

Options:

Category values:

• Security — security and access controls
• Reliability — reliability and resilience
• CostOptimization — cost optimization
• OperationalExcellence — operational efficiency
• Performance — performance efficiency

Level values: Critical, High, Medium, Suggestion

Risk values: Error, Warning, Suggestion, None

Examples:

# All risky items in the Security pillar
python3 scripts/governance_query.py pillar -c Security --risky

# Only Critical/High-priority Error and Warning items
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 status

Report 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.

python3 scripts/governance_query.py detail --id <metric-id>
python3 scripts/governance_query.py detail --keyword <search-term>

Options:

Examples:

# Query by ID
python3 scripts/governance_query.py detail --id apbxftkv5c

# Search by keyword
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.

python3 scripts/governance_query.py resources --id <metric-id>

Options:

Examples:

# List RAM users without MFA enabled
python3 scripts/governance_query.py resources --id apbxftkv5c

# List security groups that expose high-risk ports
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

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

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 cached

# Force refresh metadata cache
python3 scripts/governance_query.py --refresh overview

# Clear 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 refresh

References