SKILLEMALL.ai

AC trade-tracker

交易追踪与归因分析 v1.1.0。从交易记录逐笔追踪盈亏、 按股票/月度/季度/年度归因、交易成本分析(佣金+滑点)、 持仓时间分析、策略标签归类、多策略对比、ASCII盈亏分布图。 Use when: 需要复盘交易记录、归因分析、 查看按股票/时间段的盈亏分布、成本分析、持仓结构。 🎉 v1.1.0 新增: - 月度/季度/年度归因分解(--by month/quarter/year) - 交易成本分析(--cost: 佣金占比、滑点估算) - 持仓时间分析(--hold: 平均持仓天数、区间分组) - 策略标签归类(--tag: 按策略标签汇总) - 多策略/多时段对比(--compare) - ASCII盈亏分布图(--chart: K线图风格) - 自动识别更多日期格式 触发关键词:交易复盘、归因分析、交易统计、投资收益、持仓分析 适用范围:CSV 交易记录(含盈亏、日期列) 运行模式:纯本地

ClawHub Agent Skills author: ChengQian v1.0.0 MIT-0 7 files body ≈ 566 tokens Open the sourceclawhub.ai analyzed 2 d ago

交易追踪与归因分析 v1.1.0。从交易记录逐笔追踪盈亏、 按股票/月度/季度/年度归因、交易成本分析(佣金+滑点)、 持仓时间分析、策略标签归类、多策略对比、ASCII盈亏分布图。 Use when: 需要复盘交易记录、归因分析、 查看按股票/时间段的盈亏分布、成本分析、持仓结构。 🎉 v1.1.0 新增: -…

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
50/100
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 50/100

    • 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
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 566 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 410: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a local trade-analysis skill that reads a user-selected CSV and optionally writes a user-selected JSON report.
    LLM: benign (high) · VirusTotal: · 19 Jul 2026