CC alibabacloud-lingjun-node-diagnose
bash prefix: export LJ_SKILL_DIR="${LJ_SKILL_DIR:-$HOME/.qoder/skills/alibabacloud-lingjun-node-diagnose}" && source "$LJ_SKILL_DIR/lib/lj_init.sh" i18n: detect language each turn (CJK ratio ≥ 0.30 → LJ_LANG=zh, else en); inject LJ_LANG=zh|en prefix before source. Intelligent diagnosis for Alibaba Cloud Lingjun (regular & hyper) compute nodes: submit diagnostic tasks (BasicCheck / NodeHardwareCheck / CheckByAiJobLogs), produce structured diagnostic reports, propose repair plans (reboot / reimage / stop / report-node-status), track fault reports (list-fault-reports / describe-fault-report / stop-node-diagnostic). Read-only: list-clusters / list-cluster-nodes / list-cluster-hyper-nodes / describe-node / describe-hyper-node / list-syslogs / list-diagnostic-results. Triggers: "lingjun diagnose", "灵骏诊断", "灵骏排障", "GPU 故障", "硬件故障", "节点异常", "诊断节点", "重启节点", "重装节点", "灵骏修复", "报障", "终止报障", "syslog", "NodeHardwareCheck", "CheckByAiJobLogs", "BasicCheck", "fault report", "stop fault diagnosis"
bash prefix: export LJSKILLDIR="${LJSKILLDIR:-$HOME/.qoder/skills/alibabacloud-lingjun-node-diagnose}" && source "$LJSKILLDIR/lib/ljinit.sh" i18n: detect…
As a process C 64/100 · Has gaps — weak spots: result and completion, execution cost
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
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.
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
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 3
-
high Dangerous commands
cmd-pipe-to-shellreferences/cli-installation-guide.md:10Downloads and executes remote code from an unrecognised host (pipe to shell)curl -fsSL --connect-timeout 10 --max-time 120 https://aliyuncli.alicdn.com/setup.sh | bash
-
high Dangerous commands
cmd-pipe-to-shellSKILL.md:36Downloads and executes remote code from an unrecognised host (pipe to shell)curl -fsSL --connect-timeout 10 --max-time 120 https://aliyuncli.alicdn.com/setup.sh | bash
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenreferences/node-state-i18n.md:3High-entropy token-like string (may be an id, hash or a credential)> Authoritative source: "Lingjun customer-facing state (OperatingState) zh-en mapping table" (DingTalk doc QOG9…Mw4, synced 2026-08-21).
Files scanned: 25. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 14690 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 40Execution cost. Instruction body is 14690 tokens: crowds the task out of the window
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 54 steps, 3 vague phrases
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 15 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (18 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 994: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -238 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 10 example trigger phrases
- +4Structure: 28 headings
- +3Step-by-step instructions: 54 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (14 of 15)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.