SKILLEMALL.ai

AD stock-analysis-china

A股持仓深度技术分析技能。当用户发送持仓截图图片、或提及持仓/股票/投资建议相关意图时,本技能自动激活。 激活条件(满足任一即触发): - 用户发送了图片(持仓截图、行情截图等) - 用户说"分析持仓"、"我的持仓怎么样"、"给投资建议"、"持仓诊断"、"我的股票"、"目前持仓" - 用户说"更新持仓"、"录入持仓" - 用户说"每日一股"或类似表述 - 任何涉及持仓查询、投资建议、持仓诊断的技术分析请求 激活后行为: 1. 若收到图片 → 优先用 AI 多模态能力直接识别截图内容 2. 若无图片但有持仓意图 → 读取 positions_portfolio.json 3. 若无持仓数据 → 告知用户并引导录入 4. 运行完整技术分析(AKShare 实时行情 + RSI/MACD/KDJ/布林带/均线) 5. 生成操作建议(持有/减仓/加仓/止盈/止损) 6. 直接在对话中输出分析结果

ClawHub Agent Skills author: lzwaang v0.1.1 MIT-0 5 files body ≈ 2 637 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
D
46/100
Unfinished process
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: 5. 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 46/100

  • 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
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 100Steps. 110 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2637 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 401: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 110 items
  • +4Has examples (40 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 2 scripts are documented

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

External checks

ClawHub: suspicious
This stock-analysis skill is coherent in purpose, but it gives broad automatic authority to inspect images, change saved portfolio data, and guide system-level setup without clear approval gates.
LLM: suspicious (high) · VirusTotal: · 29 May 2026