BF quant-stock-selector
量化选股系统 - 基于 AKShare + 多因子模型的 A 股选股工具 【核心功能】 - 六大维度综合评分（技术面 30%+ 基本面 25%+ 资金面 15%+ 筹码峰 10%+90% 集中度 10%+ 成交量震荡 10%） - 消息面分析（财联社 + 新浪财经，自动去重） - 每日自动选股（Top 3 推荐） - 详细推荐理由（技术面/基本面/资金面/筹码面/热点题材） - 胜率统计面板（每日 15:30 自动统计） - 邮件推送（推荐结果 + 胜率面板） 【预期效果】 - 胜率：70-80%（历史回测数据，不代表未来收益） - 平均收益：+10-20%（历史回测数据，不代表未来收益） - 封板概率：30-40%（历史回测数据，不代表未来收益） 【使用场景】 1. 每日 14:00 自动选股推荐 2. 每日 15:30 自动胜率统计 3. 周末消息面分析 4. 手动选股查询 【触发词】选股、推荐股票、量化选股、今日推荐、胜率统计 ⚠️【重要风险提示】⚠️ 1. 股市有风险，投资需谨慎 2. 历史业绩不代表未来收益 3. 本工具仅供参考，不构成投资建议 4. 使用者应自行承担投资风险 5. 建议设置止损，控制仓位 6. 不建议全仓单吊一只股票 【版本】v1.0.0 【作者】Quant Developer 【许可证】MIT License
As a process F 35/100 · Will not run — References files that are not bundled: LICENSE
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: 7. 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: LICENSE - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: LICENSE
- 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. 99 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1787 tokens
- 100Running it twice. No mutating operations
- low 19 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
- +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
- -229 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 584: enough signal without eating the budget
- +4Structure: 46 headings
- +3Step-by-step instructions: 99 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.