SKILLEMALL.ai

AF industry-diagnosis

行业战略诊断系统——基于《五力模型·战略力》框架,输入任意行业/品类名称,自动采集数据并生成专业诊断报告(MD格式)。触发词:"诊断XX行业"、"XX行业怎么样"、"XX品类能不能做"、"分析XX市场"、"XX行业前景"。输出8大模块:市场容量、行业趋势、竞争格局、差异化机会、综合评估、切入策略、风险警示、发展机遇。适用于创业者/品牌方/投资人的战略决策参考。

ClawHub Agent Skills author: 杨首位 v0.1.0 MIT-0 4 files body ≈ 797 tokens Open the sourceclawhub.ai analyzed 3 d ago

行业战略诊断系统——基于《五力模型·战略力》框架,输入任意行业/品类名称,自动采集数据并生成专业诊断报告(MD格式)。触发词:"诊断XX行业"、"XX行业怎么样"、"XX品类能不能做"、"分析XX市场"、"XX行业前景"。输出8大模块:市场容量、行业趋势、竞争格局、差异化机会、综合评估、切入策略、风险警示、发展机遇。…

As a process F 31/100 · Will not run — References files that are not bundled: {baseDir}/assets/qrcode.jpg

AnalyzerResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: {baseDir}/assets/qrcode.jpg
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: 2. 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: {baseDir}/assets/qrcode.jpg

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: {baseDir}/assets/qrcode.jpg
  • 0Tools and files. 1 referenced file(s) missing: {baseDir}/assets/qrcode.jpg
  • 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
  • 40Consistency. Frontmatter name (industry-diagnosis) differs from the folder (industry-diagnosis-2)
  • 100Steps. 6 steps
  • 100Execution cost. Instruction body is 797 tokens
  • 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

  • +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 5 example trigger phrases
  • +3Description length 181: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 6 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is a disclosed industry research/report-writing skill with minor usability and marketing-footer caveats, but no evidence of hidden data access or unsafe execution.
LLM: benign (high) · VirusTotal: · 25 Jun 2026