SKILLEMALL.ai

AC china-stock-quant

A-share quantitative analysis toolkit. Use when user wants to analyze Chinese stocks, backtest trading strategies, calculate technical indicators (MACD/KDJ/RSI/Bollinger), implement ETF day-trading strategies (grid trading, MA crossover, volatility), fetch A-share/ETF market data, or perform risk assessment (max drawdown, Sharpe ratio). Triggers on: A股分析, 量化交易, ETF做T, 技术指标, 回测, stock analysis, quantitative trading, MACD, KDJ, RSI, 布林带, 网格交易, akshare, 选股策略, backtest.

ClawHub Agent Skills author: Miio-Jinglin v1.0.0 MIT-0 7 files body ≈ 542 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
59/100
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

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 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
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 5 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 542 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 470: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (6 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: 91.

    External checks

    ClawHub: clean
    This is a disclosed China A-share/ETF analysis and backtesting skill with no evidence of hidden access, credential use, live trading, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026