BF stock-deep-backtest
股票深度回测（Stock Deep Backtest）技能 —— 基于 QuantAll（全A解析）MCP， 对"已回测的策略"做深度诊断：策略为什么有效/失效、最终在哪儿赚钱、能否用入场因子筛选改善。 三层能力：① 四个回测视角（summary 成绩单 / detail 横截面 / segments 持仓片段 / timeline 时序净值）； ② 原生分组（行业/市值/交易所/时间 + 热力图）；③ 因子筛选改善（单/双/多因子 → 片段收益对比）。 固定调用指令已固化为 tasks/*.json，run_task_file 可直接执行；深度分析由 scripts/ 脚本完成； 一键完整链路 run_full_attribution.py → make_report_html.py 出 HTML 报告（免责声明 + A/B 结构）。 触发：策略归因/回测诊断/策略为什么有效或失效/分组回测/首尾分析/入场因子筛选/稳健性检验/参数敏感性 /alpha 归因/因子暴露/回测复盘等深度评估关键词。 不主动在"跑个回测看收益"需求中触发——那是 strategy_backtest 的事；本技能做"之后的深度诊断"。
股票深度回测（Stock Deep Backtest）技能 —— 基于 QuantAll（全A解析）MCP， 对"已回测的策略"做深度诊断：策略为什么有效/失效、最终在哪儿赚钱、能否用入场因子筛选改善。 三层能力：① 四个回测视角（summary 成绩单 / detail 横截面 / segments 持仓片段 /…
As a process F 35/100 · Will not run — References files that are not bundled: scripts/factor-screen-final-50.xlsx
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 44. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: scripts/factor-screen-final-50.xlsx - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "status"
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: scripts/factor-screen-final-50.xlsx
- 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
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2154 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 514: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (2 code blocks)
- +3All 8 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.