BF cloudq
CloudQ — 全球首款 ITOM 领域虾,全渠道 ChatOps、全天候 AIOps、全方位 CloudOps。当用户询问"你是谁"、"cloudq是什么"、"cloudq能做什么"、"介绍一下自己"、查看云架构、查询架构目录、获取架构详情、查看架构评估结果、查看风险评估项、巡检项、云资源风险、开通智能顾问等相关操作时使用。
As a process F 28/100 · Will not run — References files that are not bundled: {免密登录链接}, 免密登录URL
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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
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medium Dangerous commands
cmd-shell-rcSKILL.md:50Writes to a shell startup fileecho 'export TENCENTCLOUD_SECRET_ID="your-secret-id"' >> ~/.bashrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:51Writes to a shell startup fileecho 'export TENCENTCLOUD_SECRET_KEY="your-secret-key"' >> ~/.bashrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:115Writes to a shell startup fileecho 'export TENCENTCLOUD_ROLE_NAME="advisor"' >> ~/.bashrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:126Writes to a shell startup fileecho 'export TENCENTCLOUD_ROLE_ARN="qcs::cam::uin/1000…ame/advisor"' >> ~/.bashrc
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low Dangerous commands
cmd-shell-rccheck_env.py:303Writes 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-rccheck_env.py:304Writes 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: 20. 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
missing-refreference to a missing file: {免密登录链接} - warning
missing-refreference to a missing file: 免密登录URL
Process rating: all ten parameters 28/100
- 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. 13 mutating operations with no state check
- 40Consistency. Frontmatter name (cloudq) differs from the folder (cloudq-1)
- 100Steps. 65 steps
- 100Execution cost. Instruction body is 3749 tokens
- low 11 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
- +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
- +5Description quotes 3 example trigger phrases
- +3Description length 165: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 65 items
- +4Has examples (24 code blocks)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.