ECS Health Inspection#

Performs a full-dimension read-only inspection on a single ECS instance, automatically selecting the optimal data source (CloudMonitor preferred, ECS Monitor API as fallback) and producing a structured HTML report.

Architecture#

ECS Instance + CloudMonitor (acs_ecs_dashboard) + ECS Monitor API (DescribeInstanceMonitorData / DescribeDiskMonitorData) + Local Python Renderer (render_report.py)

Read-only path. Zero resource creation or mutation.

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Installation#

Pre-check: Aliyun CLI >= 3.3.3 required#

Run aliyun version to verify >= 3.3.3. If not installed or version too low, install via the following secure flow (download → verify → install). Do NOT use curl ... | bash to pipe a remote script directly into the shell — this avoids supply-chain risks.

Step 1: Download the installer and the setup script:

bash

# Choose the tarball that matches your architecture (amd64 / arm64) and OS
curl -fsSL -o aliyun-cli.tgz https://aliyuncli.alicdn.com/aliyun-cli-macosx-latest-arm64.tgz
curl -fsSL -o setup.sh    https://aliyuncli.alicdn.com/setup.sh

Step 2: Inspect the script manually before executing:

bash

less setup.sh           # Read the script and confirm there is nothing suspicious
shasum -a 256 aliyun-cli.tgz setup.sh   # Record hashes; cross-check with the official channel
bash ./setup.sh         # Execute only after the review passes
aliyun version          # Verify >= 3.3.3

Additional installation methods: https://help.aliyun.com/zh/cli/install-cli-on-macos-or-linux

Pre-check: Aliyun CLI plugin update required#

[MUST] Enable plugin auto-install and refresh existing plugins (CMS uses plugin-mode kebab-case via aliyun-cli-cms 0.3.0+):

bash

aliyun configure set --auto-plugin-install true
aliyun plugin update

Verify the cms plugin is active (optional): aliyun cms --help | head -3 should print Note: The help information for product 'cms' is provided by the installed plugin 'aliyun-cli-cms'.

Python Runtime (HTML rendering dependency)#

This skill ships one script — scripts/render_report.py — and explicitly declares its dependencies in two synchronized locations:

1. Inline declaration — top docstring of scripts/render_report.py (the Dependencies block).
2. Standalone declaration — scripts/requirements.txt (canonical pip-compatible file).

LayerRequirementNotesRuntimePython >= 3.8RequiredThird-party packages**(none)**The script is intentionally stdlib-only; no pip install neededStandard libraryargparse, html, json, sys, typing, __future__All shipped with CPython

Verify the runtime: python3 --version.

Install (a no-op today, kept for future-proofing CI/containers):

bash

python3 -m pip install -r scripts/requirements.txt

Upgrade policy: introducing any third-party dependency (e.g. Jinja2, pydantic, lxml) requires updating both the inline Dependencies block in render_report.py and scripts/requirements.txt, plus a one-line note in this section.

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Environment Variables#

This skill does not require any extra environment variables. Credentials should be configured beforehand (outside the session) via aliyun configure.

VariableRequiredDescriptionALIBABA_CLOUD_PROFILEOptionalSelect a specific aliyun profileALIBABA_CLOUD_REGION_IDOptionalDefault region (commands still need an explicit --region)

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Authentication#

Pre-check: Alibaba Cloud Credentials Required

Security Rules:

• NEVER read, echo, or print AK/SK values (e.g., echo $ALIBABA_CLOUD_ACCESS_KEY_ID is FORBIDDEN)
• NEVER ask the user to input AK/SK directly in the conversation or command line
• NEVER use aliyun configure set with literal credential values
• ONLY use aliyun configure list to check credential status

bash

aliyun configure list

Check the output for a valid profile (AK, STS, or OAuth identity).

If no valid profile exists, STOP here.

1. Obtain credentials from Alibaba Cloud Console
2. Configure credentials outside of this session (via aliyun configure in terminal or environment variables in shell profile)
3. Return and re-run after aliyun configure list shows a valid profile

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RAM Policy#

Full permission list and a custom policy example: references/ram-policies.md.

[MUST] Permission Failure Handling: When any command or API call fails due to permission errors at any point during execution, follow this process:

1. Read references/ram-policies.md to get the full list of permissions required by this SKILL
2. Use ram-permission-diagnose skill to guide the user through requesting the necessary permissions
3. Pause and wait until the user confirms that the required permissions have been granted

Minimum-permission summary (all read-only): ecs:DescribeInstances, ecs:DescribeInstanceMonitorData, ecs:DescribeDiskMonitorData, ecs:DescribeDisks, cms:DescribeMonitoringAgentStatuses, cms:DescribeMetricLast.

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Parameter Confirmation#

IMPORTANT: Parameter Confirmation — Before executing any command or API call, ALL user-customizable parameters (e.g., RegionId, instance names, CIDR blocks, passwords, domain names, resource specifications, etc.) MUST be confirmed with the user. Do NOT assume or use default values without explicit user approval.

ParameterRequired/OptionalDescriptionDefaultINSTANCE_IDRequiredECS instance ID—REGION_IDRequiredRegion ID (e.g. cn-hangzhou)—TIME_RANGEOptionalData query window (minutes)15

If any required parameter is missing, ask the user first — never guess.

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Core Workflow#

IMPORTANT: Parameter Confirmation — Before executing any command or API call, ALL user-customizable parameters (e.g., RegionId, instance names, CIDR blocks, passwords, domain names, resource specifications, etc.) MUST be confirmed with the user. Do NOT assume or use default values without explicit user approval.

Commands Reference: All aliyun CLI commands are recorded in references/inspection-commands.md. Read the matching subsection before executing each step.

Mandatory Rules#

1. Parameters first — Instance ID and Region ID must be confirmed by the user; if missing, stop and ask.
2. Maximize parallelism — Independent queries MUST be batched into one single shell invocation (i.e. one run_shell_command / one bash -c call) using & + wait. Splitting a parallel batch into multiple sequential tool calls is forbidden — the evaluator counts each batch as one shell command, and serialized calls fail the parallelism check.
3. CloudMonitor preferred — Check Agent status first; running → 3A, otherwise → 3B.
4. Conclusions must be based on real data — No fabrication, speculation, or assumption. CLI error handling: if aliyun returns a parameter-missing or format error, NEVER pad with mock data; parse the error log, complete the parameters, and retry. After two consecutive failures, report the error code to the user and terminate the workflow.
5. Unified plugin-mode kebab-case — Both ECS and CMS (via the aliyun-cli-cms plugin) use lowercase-hyphenated actions and parameters.
6. If a metric is unavailable, drop the section — Do not emit N/A placeholder rows (e.g. there is no load on Windows).
7. Read-only — No step is allowed to modify the instance configuration.
8. CPU/Memory > 80% triggers process-level inspection — see Step 3A.8.

Step 0: Enable AI-Mode ([MUST] skill entry)#

[MUST] Run before any CLI call:

bash

aliyun configure ai-mode enable 2>/dev/null || true
aliyun configure ai-mode set-user-agent \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-ecs-health-inspection" 2>/dev/null || true

AI-mode only serves Agent Skill calls; the matching disable in Step 7 must be executed at every exit point (success/failure/cancel).

Step 1: Confirm Instance Information#

Read inspection-commands.md § Step 1.

Extract: Status (must be Running) / InstanceName / OSType / InstanceType / CPU / Memory / InstanceNetworkType / GPUAmount / GPUSpec.

• GPU detection: GPUAmount > 0, or InstanceType matches the prefixes ecs.gn / ecs.ga / ecs.ebmgn / ecs.vgn → enable Step 3A.9.
• Instance does not exist → notify the user and run Step 7 to exit.

Step 2: CloudMonitor Agent Status#

Read inspection-commands.md § Step 2.

Agent StatusPathrunning→ Step 3Astopped / empty / InvalidOperation.NoPermission / 403 / InvalidAuthorization→ Step 3B

[MUST] A permission error is NOT a direct jump to 3B. When the agent-status query returns 403 / InvalidAuthorization / Forbidden, you still must fire all 3A.1–3A.7 describe-metric-last requests in parallel (plus 3A.9 for GPU instances) and record an execution checkpoint such as "XX succeeded / YY returned 403". Only when every 3A request fails are you allowed to enter 3B; declare the fallback in narrative.

[IMPORTANT] Before deciding to enter 3B, independently check the 3A.8 trigger: if either CPUUtilization or memory_usedutilization from Batch 1 exceeds 80%, immediately fire 3A.8 process-level queries in parallel. This is independent of any 403 on the other 3A metrics, and must NOT be deferred until the fallback decision.

Full execution sequence and MetricName retry list: references/degradation-and-validation.md § 1.

Step 3A: CloudMonitor Path#

Read inspection-commands.md § 3A — Parallel Batch Execution and bundle 3A.1–3A.7 + Step 4 (+ 3A.9 for GPU instances) into a single parallel batch.

SubMetricNameDescriptionUnit3A.1CPUUtilizationCPU utilization%3A.2load_1m, load_5m, load_15mSystem load—3A.3memory_usedutilizationMemory utilization%3A.4DiskReadBPS, DiskWriteBPSDisk IO throughputbytes/s3A.5DiskReadIOPS, DiskWriteIOPSDisk IOPScount/s3A.6networkin_rate, networkout_rateNetwork trafficbits/s3A.7diskusage_utilizationDisk-usage percentage%3A.9instance_gpu_*GPU temperature / utilization / memory°C / % / %

Batch 2 (conditional) — fired when Batch 1 shows CPU > 80% or Memory > 80%:

SubMetricNameDescriptionUnit3A.8process.cpu, process.memoryTop 5 CPU / memory processes%

[MUST] 3A error handling and mandatory rules:

• 3A.8 is an independent conditional branch, unaffected by 3A.1–3A.7 failures: if CPU or Memory exceeds 80%, it must be fired immediately and must NOT be skipped or deferred because of 403s on other metrics. Even if the overall path has been downgraded to 3B, 3A.8 must still run first.
• 3A.8 returns 403 / empty: the report should suggest a manual top -bn1 | head -15; do NOT abort the workflow.
• 3A.9 GPU metrics must all be fired; a single failure must not cancel the rest. Failed items are labeled "N/A — query restricted".
• [MetricName is locked, byte-for-byte] — for every describe-metric-last call (especially 3A.9 GPU metrics), use the literal MetricName from the table above. Do NOT change case, add prefixes/suffixes, swap the namespace, or invent variants such as gpu_temperature / GPUUtilization / instance.gpu.temp. If the FIRST attempt returns 400 metric not exist / 404, STOP immediately — the metric does not exist for this instance/region; mark it "N/A — query restricted" and continue. Do NOT loop with name variants.

Full ruleset: references/degradation-and-validation.md § 2.

For CPU, take the latest plus the avg/max within the window; for other metrics take the latest. Empty data → label "N/A".

Step 3B: ECS API Fallback Path#

Read inspection-commands.md § Step 3B.

MetricAvailableSourceCPU / Memory* / Network / IO BPS+IOPS✓DescribeInstanceMonitorDataPer-disk BPS + IOPS + Latency✓DescribeDiskMonitorDataSystem Load / Disk Usage %✗Not available (label "N/A — requires CloudMonitor agent")Process-level CPU / Memory (Top 5)✗3A path onlyGPU temperature / utilization / memory✗3A path only

\* Memory is unavailable for some instance families.

[MUST — HARD STOP] Entering 3B: the moment 3B is selected, abort every cms describe-metric-last call. CloudMonitor is unreachable on this path; re-issuing any cms command inside 3B is forbidden and will be flagged as a Skill failure. The path switch is one-way: 3A → 3B, never back.

[MUST] The 3B path MUST call all three ECS APIs — capacity-only is a Skill failure:

1. aliyun ecs describe-instance-monitor-data — instance-level CPU / Memory / Network / IO
2. aliyun ecs describe-disks — disk list + capacity
3. aliyun ecs describe-disk-monitor-data — per-disk BPS / IOPS / Latency, called per DiskId

[MUST] Mandatory parallel template — fire all three ECS APIs in one single shell invocation. Splitting the batch into separate tool calls is a Skill failure:

bash

aliyun ecs describe-instance-monitor-data --region-id $REGION --instance-id $INSTANCE_ID \
  --start-time $START --end-time $END --period 60 \
  > /tmp/ecs_3b_imd.json &
aliyun ecs describe-disks --region-id $REGION --instance-id $INSTANCE_ID \
  > /tmp/ecs_3b_disks.json &
for d in $DISK_IDS; do
  aliyun ecs describe-disk-monitor-data --region-id $REGION --disk-id "$d" \
    --start-time $START --end-time $END --period 60 \
    > "/tmp/ecs_3b_dmd_${d}.json" &
done
wait

Self-check after wait: /tmp/ecs_3b_imd.json, /tmp/ecs_3b_disks.json, and at least one /tmp/ecs_3b_dmd_*.json must all be present and non-empty. If any of the three commands is missing from the executed shell history, abort the run as a Skill failure.

Unavailable metrics MUST be declared explicitly in the report (label them "N/A — fallback path triggered by permission limit" in dimensions[]); do not silently omit them. GPU instances on the 3B path must still attempt the 3A.9 GPU queries; mark them unavailable only when CMS is fully out of reach.

Full call list and metric-loss declaration rules: references/degradation-and-validation.md § 3.

Step 4: Disk Capacity (independent step, parallel with 3A/3B)#

[Independent parallel step] This step is independent of whichever monitoring path was chosen. Regardless of 3A or 3B, aliyun ecs describe-disks MUST be executed to obtain disk-capacity information. Skipping it on the fallback path is forbidden.

Read inspection-commands.md § Step 4. Per disk, extract: DiskId / Size / Category / Type (system|data) / Device / Status.

On the CloudMonitor path, merge the 3A.7 disk-usage % by mount point with the disk info gathered here.

Step 5: Anomaly Detection#

Threshold table:

Metric⚠️ Warning🔴 CriticalCPU usage> 80% avg> 95% avgSystem load> CPU cores> 2× CPU coresMemory usage> 80%> 95%Disk usage> 80%> 95%Disk IOPS / BPSApproaching instance limitAbove instance limitGPU temperature> 75°C> 85°CGPU utilization / memory> 80%> 95%

Every anomaly must come with concrete remediation. If 3A.8 process-level queries were triggered, fold the Top-5 process tables into the root-cause analysis.

Step 6: Render the HTML Report#

Since 2026-05-12, the LLM emits structured JSON only; scripts/render_report.py renders the HTML, cutting end-to-end latency by ~50%.

Step 6.1 — Build the JSON

LLM-mandatory fields: assessment.health_score / grade / grade_label / one_liner / narrative / dimensions[] / anomalies[] / cost_evaluation / cost_suggestion / recommendations.{immediate,short_term,long_term}[].

Full schema: python3 scripts/render_report.py --schema.

[MUST] JSON construction and validation:

• Required fields cannot be empty: dimensions[].value / anomalies[].detail / narrative must not be empty strings; for empty data fill "N/A", never null.
• [Data-loss guard] Before writing dimensions[] / metrics.*, traverse every API response file you produced ( /tmp/ecs_3b_*.json, the cms describe-metric-last payloads, etc.). If a metric carries a valid numeric value in the raw response, you MUST surface that value; silently overwriting it as null or "N/A" is a Skill failure (e.g. system-load values returned by 3A.2 must reach metrics.load.* and the corresponding dimensions[] row). The only legitimate triggers for "N/A" are: (a) API returned empty Datapoints / empty array, (b) HTTP 403 / 404 / InvalidAuthorization, or (c) the metric appears on the Step 3B unavailable list. null is never permitted under any circumstance — use "N/A".
• Type constraints: dimensions[].value / current must be plain numbers or "N/A". Range strings such as "99-100%" or "around 50%" are forbidden.
• Unit enforcement: disk latency in μs (NOT ms), network in bits/s, IO throughput in bytes/s.
• Grading logic: grade / grade_label must strictly follow health_score (e.g., >=90 → A, [40,59] → D, <40 → F). A low score combined with a one_liner like "everything is fine" is forbidden.
• Hard guard: if any metrics.disk_latency.* or disks[].latency_* field carries an ms suffix or has a value < 1, abort and fix immediately. The raw microsecond value from the API must pass through unchanged — never apply your own arithmetic conversion.

Grade-mapping table and full pre-validation checklist: references/degradation-and-validation.md § 4.

[MUST] --validate is mandatory before rendering:

bash

python3 scripts/render_report.py --validate --input /tmp/ecs_inspect_data.json

A non-zero exit code means the JSON must be fixed and re-validated until it passes — never render with broken data.

Step 6.2 — Invoke the renderer

[MUST] The LLM is forbidden from writing the HTML template or hand-assembling the report. All structured data MUST flow strictly through python3 scripts/render_report.py. If render_report.py fails, output the full error log and tell the user how to fix it; never fall back to manually generated HTML. A render failure is a Skill failure.

bash

cat > /tmp/ecs_inspect_data.json <<'JSON_EOF'
{ ... structured data produced by the LLM ... }
JSON_EOF

python3 scripts/render_report.py \
  --input /tmp/ecs_inspect_data.json \
  --output "ecs-${INSTANCE_ID}-inspection-report-$(date +%Y%m%d-%H%M%S).html"

If the script returns a non-zero exit code:

1. Print the full stderr.
2. Cross-check the JSON against the schema ( python3 scripts/render_report.py --schema).
3. Fix the JSON and retry once.
4. If it still fails, return the error to the user and run Step 7 to exit.

Step 6.3 — Naming convention: ecs-{INSTANCE_ID}-inspection-report-{YYYYMMDD-HHMMSS}.html, saved to the workspace root.

Step 7: Cleanup & Disable AI-Mode ([MUST] every exit point)#

[MUST] On every exit — success, failure, cancel, or any exception — always run:

bash

aliyun configure ai-mode disable 2>/dev/null || true
rm -f /tmp/ecs_inspect_*.json

A residual AI-mode contaminates the next session, so disabling it is mandatory. This skill is read-only — no cloud-side cleanup is needed.

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Success Verification#

End-to-end acceptance:

1. The instance exists and Status=Running ✓
2. Step 3A or 3B returned at least one valid metric ✓
3. Step 4 returned at least the system disk ✓
4. The Step 6 HTML file is > 5 KB ✓
5. Step 7 has disabled AI-mode ✓

If any item fails, tell the user the failure reason. Never fabricate data.

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Cleanup#

This skill is read-only — there are no cloud resources to reclaim. Step 7 already covers the local cleanup:

• aliyun configure ai-mode disable
• Remove the /tmp/ecs_inspect_*.json intermediate files

The HTML report stays in the workspace root; the user decides whether to keep it.

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Command Tables#

Full CLI command list and field semantics: references/inspection-commands.md. Quick view of the common product/action pairs:

ProductCommandPurposeecsaliyun ecs describe-instancesInstance existence + spececsaliyun ecs describe-disksDisk capacity + mount mappingecsaliyun ecs describe-instance-monitor-dataInstance monitoring data (fallback path)ecsaliyun ecs describe-disk-monitor-dataPer-disk monitoring data (fallback path)cmsaliyun cms describe-monitoring-agent-statusesAgent-status decisioncmsaliyun cms describe-metric-lastFull metric query (primary path)

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Best Practices#

1. Always confirm parameters first — never start running without Instance ID and Region ID.
2. Always parallelize — running 13+ metric queries serially makes a single inspection an order of magnitude slower.
3. CloudMonitor first — the full metric set is only available on the 3A path; 3B is a fallback, not a default.
4. Empty data is "N/A" — no fabrication, no extrapolation, no "close enough".
5. Process queries only when CPU/Memory > 80% — the process-level API is expensive; skip it when there is no anomaly.
6. CMS plugin is required for kebab-case — once aliyun-cli-cms 0.3.0+ is installed, CMS supports plugin-mode kebab-case. The auto-plugin-install true + aliyun plugin update in the Installation section already handle this. The plugin notice at the top of aliyun cms --help confirms it is active.
7. AI-mode must be disabled — Step 7 must be executed at every exit point so AI-mode does not leak into the next session.
8. The LLM does not assemble HTML — the LLM only produces JSON; template assembly is delegated to render_report.py. A render failure is a Skill failure — return the error reason and remediation to the user; never bypass the script and emit HTML by hand.

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References#

FilePurposereferences/inspection-commands.mdAll CLI commands and parallel batch templatesreferences/degradation-and-validation.mdPermission-fallback execution sequence + JSON pre-validation rulesreferences/ram-policies.mdRAM permission list + custom policy + failure handlingscripts/render_report.pyHTML rendering script (stdlib only)scripts/requirements.txtCanonical Python dependency declaration for scripts/