SKILLEMALL.ai

BF smarttable-weekly-check

企业微信智能表格周报检查工具。通过 Puppeteer 启动浏览器,利用内部 API 获取结构化数据,按4条规则检查组员周报质量。支持多周链接批量检查、自动登录等待、自动报告生成、项目维度汇总。触发词:智能表格周报检查、小组周报检查、周报链接检查、检查周报链接、smarttable check、数字员工周报检查。

ClawHub Agent Skills author: imchen7626-create v1.1.0 MIT-0 8 files body ≈ 1 128 tokens Open the sourceclawhub.ai analyzed 2 d ago

企业微信智能表格周报检查工具。通过 Puppeteer 启动浏览器,利用内部 API 获取结构化数据,按4条规则检查组员周报质量。支持多周链接批量检查、自动登录等待、自动报告生成、项目维度汇总。触发词:智能表格周报检查、小组周报检查、周报链接检查、检查周报链接、smarttable check、数字员工周报检查。

As a process F 35/100 · Will not run — References files that are not bundled: scripts/extract_weekly.js

IntegrationPlaywrightData and analyticsSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/extract_weekly.js
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

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 · 0

✓ No critical or high findings

Files scanned: 0. 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: scripts/extract_weekly.js

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/extract_weekly.js
  • 0Tools and files. 1 referenced file(s) missing: scripts/extract_weekly.js
  • 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
  • 100Steps. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1128 tokens
  • 100Running it twice. No mutating operations
  • low 12 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 157: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
The skill is purpose-aligned and disclosed, but it stores employee weekly-report data and a reusable browser login profile locally.
LLM: benign (high) · VirusTotal: · 25 Jun 2026