Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/feicoder/skill-factory/strategy-selectionnpx skills add FeiCoder/Skill-Factory --skill strategy-selectiongit clone --depth 1 https://github.com/FeiCoder/Skill-FactoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00057 | $0.03198 |
| Opus 5 | $0.00028 | $0.01599 |
| Sonnet 5 | $0.00011 | $0.00640 |
| Haiku 4.5 | $0.00006 | $0.00320 |
Grade A, and why
strategy-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
策略组合选择
概述
量化投资不是单策略打天下，而是需要根据市场环境变化动态调整策略组合。本技能提供一套系统化的方法，帮助你判断当前市场环境并选择合适的策略搭配。
何时使用此技能：
- 当你拥有多个策略但不知道如何组合使用时
- 当市场环境发生变化需要调整策略时
- 当你需要构建一个稳健的量化投资系统时
- 当你想了解策略与市场环境的匹配关系时
核心框架：三层决策体系
┌─────────────────────────────────────────────────────────┐
│ 战略层：资产配置 │
│ (决定股票/债券/商品/现金的配置比例) │
├─────────────────────────────────────────────────────────┤
│ 战术层：策略选择 │
│ (决定选股策略+择时策略的组合) │
├─────────────────────────────────────────────────────────┤
│ 执行层：参数优化 │
│ (具体因子参数、阈值调优) │
└─────────────────────────────────────────────────────────┘
本技能聚焦于"战术层"：如何选择合适的策略组合
第一步：判断市场环境
四要素判断法
| 指标 | 牛市特征 | 熊市特征 | 震荡市特征 |
|---|---|---|---|
| 价格趋势 | 均线多头排列 | 均线空头排列 | 均线缠绕 |
| 波动率 | 适中（20%~25%） | 急剧放大（>30%） | 较低（<20%） |
| 成交量 | 放大 | 萎缩或恐慌放量 | 缩量 |
| 市场情绪 | 乐观贪婪 | 恐慌悲观 | 观望犹豫 |
快速判断指标
# 市场环境判断示例
def judge_market_regime():
"""
返回: 'bull' | 'bear' | '震荡'
"""
# 1. 趋势判断：20日均线 vs 60日均线
ma20 = close_price.rolling(20).mean()
ma60 = close_price.rolling(60).mean()
trend = 'bull' if ma20 > ma60 else 'bear'
# 2. 波动率判断
volatility = returns.rolling(20).std() * np.sqrt(252)
# 3. 综合判断
if volatility < 0.2:
regime = '震荡'
else:
regime = trend
return regime
环境持续时间统计
| 市场环境 | 平均持续时间 | 策略有效期 |
|---|---|---|
| 牛市 | 6~12个月 | 3~6个月需评估 |
| 熊市 | 3~8个月 | 1~3个月需评估 |
| 震荡市 | 2~6个月 | 1~2个月需评估 |
第二步：策略-环境匹配矩阵
选股策略 × 市场环境
| 选股策略 | 牛市 | 熊市 | 震荡市 | 说明 |
|---|---|---|---|---|
| 多因子模型 | ⭐⭐⭐ 推荐 | ⭐⭐ 谨慎 | ⭐⭐⭐ 推荐 | 牛市高 Alpha，震荡市防守性强 |
| 动量策略 | ⭐⭐⭐ 推荐 | ⭐ 回避 | ⭐⭐ 适用 | 动量在趋势市中有效 |
| 反转策略 | ⭐ 谨慎 | ⭐⭐ 适用 | ⭐⭐⭐ 推荐 | 震荡市均值回复特征强 |
| 资金流策略 | ⭐⭐⭐ 推荐 | ⭐⭐ 适用 | ⭐⭐ 适用 | 资金流入是牛市发动机 |
| 行业轮动 | ⭐⭐⭐ 推荐 | ⭐⭐ 适用 | ⭐⭐⭐ 推荐 | 任何环境都有轮动机会 |
| 风格轮动 | ⭐⭐ 适用 | ⭐⭐ 适用 | ⭐⭐⭐ 推荐 | 震荡市风格切换频繁 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 317 lines · 57 tokens per session scan A 9c012ad865d8
strategy-selection is a skill published in the GitHub repository FeiCoder/Skill-Factory (10 stars, last pushed 6mo ago), licensed MIT. It adds 57 tokens to every session and 3,198 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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