AD qq-group-homework-summarizer
QQ群作业整理 —— 从 QQ 群「群作业」抓取指定日期的作业内容（含图片附件），生成排版规范的 A4 Word 文档（默认单页、可多页，支持多群合并、科目筛选、仅文字版），并可发送到指定邮箱或微信；内置 doctor 环境自检（CLI/daemon/连通性/登录态/产物），适合挂无人值守定时任务。This skill should be used when the user asks to organize, export, or send QQ group homework (群作业) as a document — for example "把X月X日的群作业整理成Word"、"把今天的作业导出成文档"、"把作业发到我邮箱/微信"。触发词：群作业、作业整理、作业文档、QQ作业、发作业给XX。
QQ群作业整理 —— 从 QQ 群「群作业」抓取指定日期的作业内容（含图片附件），生成排版规范的 A4 Word 文档（默认单页、可多页，支持多群合并、科目筛选、仅文字版），并可发送到指定邮箱或微信；内置 doctor 环境自检（CLI/daemon/连通性/登录态/产物），适合挂无人值守定时任务。This…
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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
- 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 · 1
✓ No critical or high findings
Medium and low: 1
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medium Dangerous commands
cmd-install-from-urlreferences/troubleshooting.md:52Installs a package from an untrusted URL / archive (detector / deny-list definition)- 安装**失败** → 改用阿里云镜像源：`pip install --index-url https://mirrors.aliyun.com/pypi/simple/ --upgrade qqbrowser-skill`
detector
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "display_name_en" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 4 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3458 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -235 emoji in the instructions: noise for the model
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 353: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (19 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.