SKILLEMALL.ai

BC perspective-router

Nova 视角调度引擎 — 根据任务描述自动匹配最合适的专家视角能力。 当 Nova 收到复杂、多领域、需要专业深度分析的任务时,自动调用此路由。 工作原理: 1. 分析任务文本中的关键词和语义 2. 在视角库(12位专家心智模型)中打分排序 3. 返回最匹配的 1-3 个视角,附调用建议 4. Nova 读取对应 SKILL.md,以该专家身份深度分析 5. 整合专家视角进主回复,保留 Nova 最终判断权 适用任务类型: - 投资/理财决策(含 Naval/Munger) - 战略/工程/颠覆式创新(含 Elon Musk) - 商业分析/风险评估(含 Munger) - 创业/产品/写作(含 Paul Graham) - 产品设计/品牌/体验(含 Steve Jobs) - 谈判/影响力/直接说服(含 Trump) - 内容传播/流量/注意力经济(含 MrBeast) - 反脆弱/风险管理/黑天鹅(含 Taleb) - 深度学习/AI/神经网络(含 Karpathy) 触发词:「用X视角分析」「涉及投资决策」「这个战略怎么样」「商业分析」等

ClawHub Agent Skills author: CatPluZ v1.0.0 MIT-0 6 files · 1 script body ≈ 767 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
C
53/100
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: 6. 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")

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 9 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 767 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

  • +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
  • -212 emoji in the instructions: noise for the model
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 483: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This is a local keyword-based router for choosing expert-style perspectives, with broad activation wording but no evidence of hidden access, persistence, credential use, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026