SKILLEMALL.ai

BD akshare_backtest

A股量化策略回测工具。基于 AkShare 获取历史行情数据,模拟执行强势股轮动策略。 支持自定义初始资金、回测周期、止盈止损参数。输出收益曲线、买卖记录、月度统计。 适用于验证"涨停基因+均线多头+量价配合"等短线策略的历史表现。

ClawHub Agent Skills author: Gingin v1.0.2 MIT-0 3 files body ≈ 423 tokens Open the sourceclawhub.ai analyzed 2 d ago

A股量化策略回测工具。基于 AkShare 获取历史行情数据,模拟执行强势股轮动策略。 支持自定义初始资金、回测周期、止盈止损参数。输出收益曲线、买卖记录、月度统计。 适用于验证"涨停基因+均线多头+量价配合"等短线策略的历史表现。

As a process D 49/100 · 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
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
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

The same skill appears in 1 more place: ClawHub

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 name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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 (akshare_backtest) differs from the folder (backtest)
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Execution cost. Instruction body is 423 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)
  • +3Description length 116: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented

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

External checks

ClawHub: clean
This is a coherent stock backtesting skill that installs common Python data packages, fetches market data, and writes local CSV results.
LLM: benign (high) · VirusTotal: · 29 May 2026