SKILLEMALL.ai

BC industry-chain-intelligence

企业决策者产业链战略报告系统。面向企业CEO/CTO/战略总监,输入公司名称或产业链, 一键生成10页白皮书风格HTML报告,涵盖产业链定位、上游卡脖子风险、下游市场格局、 技术竞争态势、科创力雷达、趋势机会及行动路线图。 视觉风格严格遵循 coolingstyle_v2_whitepaper.html 白皮书 DNA:象牙白底、深海军蓝标题、 朱红强调、古金装饰、Noto Serif/Sans/Roboto Mono 字体三件套、960px宽/52px页边距。 数据通过智慧芽MCP实时获取,图表默认统计范围2012-当前年月。

ClawHub Agent Skills author: yuanzhian-patsnap v1.0.0 MIT-0 4 files body ≈ 4 642 tokens Open the sourceclawhub.ai analyzed 3 d ago

企业决策者产业链战略报告系统。面向企业CEO/CTO/战略总监,输入公司名称或产业链, 一键生成10页白皮书风格HTML报告,涵盖产业链定位、上游卡脖子风险、下游市场格局、 技术竞争态势、科创力雷达、趋势机会及行动路线图。 视觉风格严格遵循 coolingstylev2whitepaper.html 白皮书…

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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: 4. 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")
  • note frontmatter-key unknown frontmatter key "copyright"

Process rating: all ten parameters 52/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 4642 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 206 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations

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
  • -241 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 267: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 206 items
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
The skill is a local HTML report generator, but its bundled generator appears to produce hard-coded business intelligence while presenting the output as real-time Zhihuiya MCP data.
LLM: suspicious (high) · 13 Aug 2026