SKILLEMALL.ai

BC vegas-tunnel-free

A股信息分散难决策。维加斯通道交易-EMA+斐波那契+共振评分,A股场景效率提升3倍。。> **核心功能**: 本技能提供与边界情况、化工作流与智能决策辅助等能力。。适用于多种工作场景,提供专业的能力支持。轻量级设计,低资源占用,适配云端与本地部署。开箱即用,无需复杂配置,支持中文交互与结构化输出。

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 2 697 tokens Open the sourceclawhub.ai analyzed 3 d ago

A股信息分散难决策。维加斯通道交易-EMA+斐波那契+共振评分,A股场景效率提升3倍。。> 核心功能: 本技能提供与边界情况、化工作流与智能决策辅助等能力。。适用于多种工作场景,提供专业的能力支持。轻量级设计,低资源占用,适配云端与本地部署。开箱即用,无需复杂配置,支持中文交互与结构化输出。

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

IntegrationAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
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: 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"

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. Tools declared in frontmatter
  • 100Steps. 127 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2697 tokens
  • 100Running it twice. No mutating operations
  • low 23 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
  • +2Single-language instructions
  • +3Description length 150: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 127 items
  • +4Has examples (6 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This trading-analysis skill is not clearly malicious, but it asks for broad execution authority and discusses API keys and live trading without enough concrete boundaries or safeguards.
LLM: suspicious (high) · 15 Aug 2026