SKILLEMALL.ai

AD investment-decision-system

个人投资决策辅助系统。基于 INVEST 六维决策框架(意图/数字/价值/优势/安全/时机), 将分散的市场信息、持仓数据、投资目标和风控纪律串成一个完整的决策闭环。 提供持仓管理、决策评分、仓位计算、风控检查和交互式 HTML 可视化报告。 触发词:投资决策, 买卖决策, 持仓分析, 投资复盘, 仓位计算, 风控检查, INVEST, 投资仪表盘, 添加持仓, 记录交易, 买入分析, 卖出决策, 投资目标。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 7 files body ≈ 1 609 tokens Open the sourceclawhub.ai analyzed 34 h ago

个人投资决策辅助系统。基于 INVEST 六维决策框架(意图/数字/价值/优势/安全/时机), 将分散的市场信息、持仓数据、投资目标和风控纪律串成一个完整的决策闭环。 提供持仓管理、决策评分、仓位计算、风控检查和交互式 HTML 可视化报告。 触发词:投资决策, 买卖决策, 持仓分析, 投资复盘, 仓位计算…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
43/100
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: 7. 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 43/100

  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1609 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 206: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This is a coherent local investment tracking and decision-support skill, but users should treat its financial outputs as informational rather than personalized advice.
LLM: benign (medium) · VirusTotal: · 20 Jun 2026