SKILLEMALL.ai

BD quant-strategy-interpreter

量化交易与期货策略解读助手。触发词:量化策略、回测、期货策略、双均线、海龟交易法则、均值回归、布林带、动量突破、跨期套利、配对交易、统计套利、策略参数、文献解读、英文论文策略复现。把英文量化/期货文献中的策略思想转化为中文解读 + 可运行回测模板(Python/pandas)+ 参数说明 + 文献出处,并调用「数据查询 MCP」取真实行情回测。

ClawHub Agent Skills author: yingzi6776-cmd v1.0.0 MIT-0 21 files body ≈ 855 tokens Open the sourceclawhub.ai analyzed 2 d ago

量化交易与期货策略解读助手。触发词:量化策略、回测、期货策略、双均线、海龟交易法则、均值回归、布林带、动量突破、跨期套利、配对交易、统计套利、策略参数、文献解读、英文论文策略复现。把英文量化/期货文献中的策略思想转化为中文解读 + 可运行回测模板(Python/pandas)+ 参数说明 +…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
49/100
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: 21. 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 "display_name"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (quant-strategy-interpreter) differs from the folder (strategylens-quant-kit)
  • 100Tools and files. No external tools needed
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 855 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
  • -33 of 12 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 173: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (5 of 7)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This skill is a disclosed quantitative-finance research and backtesting helper, with no hidden trading, credential, persistence, or destructive behavior found.
LLM: benign (high) · VirusTotal: · 25 Jul 2026