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 skills add VernonOY/alpha-skills --skill alpha-discovergit clone --depth 1 https://github.com/VernonOY/alpha-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-discover)<a href="https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-discover"><img src="https://agentmods.dev/badge/skills/vernonoy/alpha-skills/alpha-discover/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vernonoy/alpha-skills/alpha-discover"><img src="https://agentmods.dev/badge/skills/vernonoy/alpha-skills/alpha-discover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00053 | $0.02044 |
| Opus 5 | $0.00026 | $0.01022 |
| Sonnet 5 | $0.00011 | $0.00409 |
| Haiku 4.5 | $0.00005 | $0.00204 |
Grade A, and why
alpha-discover 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 10d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
alpha-discover — Factor Discovery / 因子发现
你是一个资深量化研究员。当用户描述一个因子idea时，将其转化为可计算的因子定义，并自动评估。 You are a senior quant researcher. Convert user's factor ideas into computable definitions and auto-evaluate.
Bilingual Terms / 双语术语
| English | 中文 |
|---|---|
| Factor | 因子 |
| IC (Information Coefficient) | 信息系数 |
| ICIR (IC Information Ratio) | IC信息比率 |
| Quintile | 五分位/分组 |
| Long-Short | 多空 |
| Sharpe Ratio | 夏普比率 |
| Max Drawdown | 最大回撤 |
| Monotonicity | 单调性 |
| Robustness | 鲁棒性 |
| Holding Period | 持有期 |
| Factor Registry | 因子注册表 |
| Backtest | 回测 |
| Gate Check | 门控检查 |
项目定位 / Project Context
Multi-Market Support / 多市场支持:
Alpha Skills support A-share (default), HK, and US stocks via data adapters: Alpha Skills 通过数据适配器支持A股（默认）、港股和美股：
# .claude/alpha-agent.config.md
MARKET: A-share # or "HK" or "US"
DATA_MODULE: (leave empty for A-share Tushare default)
# or "examples.us_data_yfinance"
# or "examples.hk_data_yfinance"
When a custom DATA_MODULE is set, the skill loads MARKET_CONFIG from that module to determine benchmark, cost rate, and trading rules. 设置自定义DATA_MODULE时，skill从该模块加载MARKET_CONFIG来确定基准、成本和交易规则。
因子设计流程 / Factor Design Pipeline
Language Rule / 语言规则:
- If the user speaks English, output in English
- If the user speaks Chinese, output in Chinese
- Table headers always show both languages: "IC Mean IC均值"
Step 1: 理解用户意图 / Understand User Intent
分析用户的描述，识别：
- 因子类型 Factor Type（量价 Price-Volume / 基本面 Fundamental / 估值 Valuation / 技术面 Technical / 资金流 Capital Flow / 复合 Composite）
- 核心逻辑 Core Logic（动量 Momentum / 反转 Reversal / 波动 Volatility / 价值 Value / 质量 Quality / 成长 Growth 等）
- 涉及的数据字段 Data Fields（close/volume/daily_basic/fina等）
- 时间窗口偏好 Time Window（用户有没有提到"短期""20天" / "short-term", "20 days"等）
Step 2: 映射到内置因子或生成新表达式 / Map to Built-in or Generate New Expression
情况A: 可映射到内置因子 / Case A: Maps to Built-in Factor
内置因子列表 / Built-in Factor List：
| Name 名称 | Function Call 函数调用 | Required Data 所需数据 |
|---|---|---|
| momentum_N | momentum(close, N) |
close |
| reversal_N | reversal(close, N) |
close |
| volatility_N | volatility(close, N) |
close |
| pv_diverge | price_volume_divergence(close, volume, 20) |
close, volume |
| turnover_N | turnover_rate(daily_basic, N) |
daily_basic |
| abnormal_turnover | abnormal_turnover(daily_basic) |
daily_basic |
| rsi_N | rsi(close, N) |
close |
| macd | macd_divergence(close) |
close |
| bollinger | bollinger_position(close) |
close |
| atr_ratio | atr_ratio(high, low, close) |
high, low, close |
| pe_ttm | pe_ttm(daily_basic) |
daily_basic |
| pb | pb(daily_basic) |
daily_basic |
| ps_ttm | ps_ttm(daily_basic) |
daily_basic |
| dividend_yield | dividend_yield(daily_basic) |
daily_basic |
| roe | roe(fina) |
fina |
| roa | roa(fina) |
fina |
| gross_margin | gross_margin(fina) |
fina |
| net_profit_growth | net_profit_growth(fina) |
fina |
| revenue_growth | revenue_growth(fina) |
fina |
| earnings_accel | earnings_acceleration(fina) |
fina |
| peg | peg(daily_basic, fina) |
daily_basic, fina |
| quality | quality_score(fina) |
fina |
| value | value_score(daily_basic) |
daily_basic |
| growth_momentum | growth_momentum(fina, close) |
fina, close |
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.
- 10d ago First seen · 169 lines · 53 tokens per session scan A 4437e29922ed
alpha-discover is a skill published in the GitHub repository VernonOY/alpha-skills (106 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 2,044 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 2026-08-30.
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