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BC alibabacloud-aes-sysom-lingjun-diagnosis

Perform SysOM deep OS-level diagnosis on Alibaba Cloud lingjun node to identify root causes of performance issues (CPU spikes, memory leaks, IO latency, etc.). Use when users report lingjun node performance problems, need kernel-level troubleshooting, or want to configure DingTalk alert notifications for SysOM diagnosis reports.

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 12 files · 1 script body ≈ 6 910 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Perform SysOM deep OS-level diagnosis on Alibaba Cloud lingjun node to identify root causes of performance issues (CPU spikes, memory leaks, IO latency…

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticsInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
80
Quality 40%
79
Run on models
none yet
Process rating
C
63/100
Has gaps
When it triggers w 12
20
Inputs and preconditions w 11
30
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 4

✓ No critical or high findings

Medium and low: 4
  • medium Dangerous commands cmd-privilege references/cli-installation-guide.md:30
    Privilege escalation / world-writable permissions
    sudo mv aliyun /usr/local/bin/
  • medium Dangerous commands cmd-privilege references/cli-installation-guide.md:45
    Privilege escalation / world-writable permissions
    sudo mv aliyun /usr/local/bin/
  • medium Dangerous commands cmd-privilege references/cli-installation-guide.md:58
    Privilege escalation / world-writable permissions
    sudo mv aliyun /usr/local/bin/
  • medium Dangerous commands cmd-privilege references/cli-installation-guide.md:71
    Privilege escalation / world-writable permissions
    sudo mv aliyun /usr/local/bin/

Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6910 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 63/100

  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 13 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6910 tokens
  • 100Steps. 38 steps
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (15 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -220 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 330: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (18 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
This skill is mostly aligned with Alibaba Cloud SysOM diagnosis, but it can change local tooling settings and cloud alert/SysOM state while explicitly suppressing confirmation and partly describing diagnosis as read-only.
LLM: suspicious (high) · 20 Aug 2026