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LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 18 files · 1 script body ≈ 3 620 tokens Open the sourcegithub.com↗ analyzed 2 d ago

用户咨询腾讯云产品资源、AWS、阿里云等多云资源时，查看智能顾问架构图、架构目录、架构详情、架构评估结果、绘制架构图、开通智能顾问时、AI智能巡检、AI容量监测、AI混沌演练、AI云诊断、主动预警、架构健康度、云运维问答、云资源查询、云成本优化、安全合规、云资源盘点、闲置资源检查、云产品最佳实践等AIOps、Chat…

As a process F 33/100 · Will not run — References files that are not bundled: {免密登录链接}, 免密登录URL

ProcedureAWSInfrastructuretype 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
C
73/100
safety, quality, tests
Safety 60%
78
Quality 40%
66
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: {免密登录链接}, 免密登录URL
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
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. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Dangerous commands cmd-shell-rc SKILL.md:62
    Writes to a shell startup file
    echo 'export TENCENTCLOUD_SECRET_ID="your-secret-id"' >> ~/.zshrc
  • medium Dangerous commands cmd-shell-rc SKILL.md:63
    Writes to a shell startup file
    echo 'export TENCENTCLOUD_SECRET_KEY="your-secret-key"' >> ~/.zshrc
  • medium Dangerous commands cmd-shell-rc SKILL.md:120
    Writes to a shell startup file
    echo 'export TENCENTCLOUD_ROLE_NAME="advisor"' >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc SKILL.md:131
    Writes to a shell startup file
    echo 'export TENCENTCLOUD_ROLE_ARN="qcs::cam::uin/1000…ame/advisor"' >> ~/.bashrc
  • low Dangerous commands cmd-shell-rc check_env.py:366
    Writes to a shell startup file (string literal in code, not executed)
    log_info('    echo \'export TENCENTCLOUD_SECRET_ID="your-secret-id"\' >> ~/.bashrc')
    code literal
  • low Dangerous commands cmd-shell-rc check_env.py:367
    Writes to a shell startup file (string literal in code, not executed)
    log_info('    echo \'export TENCENTCLOUD_SECRET_KEY="your-secret-key"\' >> ~/.bashrc')
    code literal

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: {免密登录链接}
  • warning missing-ref reference to a missing file: 免密登录URL

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: {免密登录链接}, 免密登录URL
  • 0Tools and files. 2 referenced file(s) missing: {免密登录链接}, 免密登录URL
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 11 mutating operations with no state check
  • 100Steps. 72 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3620 tokens
  • low 10 top-level sections: this looks like several domains in one skill

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 177: enough signal without eating the budget
  • +4Structure: 44 headings
  • +3Step-by-step instructions: 72 items
  • +4Has examples (25 code blocks)
  • +3All 6 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.