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 xyva-yuangui/XyvaClaw --skill quant-strategy-enginegit clone --depth 1 https://github.com/xyva-yuangui/XyvaClawWrote 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/xyva-yuangui/xyvaclaw/quant-strategy-engine)<a href="https://agentmods.dev/skills/xyva-yuangui/xyvaclaw/quant-strategy-engine"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/quant-strategy-engine/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/xyva-yuangui/xyvaclaw/quant-strategy-engine"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/quant-strategy-engine.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 | $0.00004 | $0.00374 |
| Opus 5.5 | $0.00002 | $0.00150 |
| Sonnet 5.5 | $0.00001 | $0.00075 |
| Haiku 4.5 | $0.00000 | $0.00037 |
Grade A, and why
quant-strategy-engine 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 1mo 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.
What it actually says
quant-strategy-engine
量化策略引擎。触发后必须先询问用户确认，再执行策略分析。
| 触发场景 | 询问确认内容 |
|---|---|
| 策略选股 | 确认选股范围 (全市场/板块)、因子类型、持仓周期 |
| 因子分析 | 确认因子类别 (估值/动量/质量)、时间窗口、可视化需求 |
| 策略回测 | 确认回测区间、初始资金、手续费率、调仓频率 |
| 信号生成 | 确认信号类型 (买入/卖出/预警)、推送方式 |
| 风控管理 | 确认止损线、仓位上限、风险敞口 |
执行流程: 触发 → 询问用户 → 用户确认 → 执行分析 → 输出报告
Full docs: read SKILL-REFERENCE.md
What ships with it
18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- _meta.json 116 B
- DEVELOPMENT_PLAN.md 5.0 KB
- DYNAMIC_WEIGHTS_PLAN.md 11 KB
- examples/README.md 720 B
- scripts/cache_manager.py 7.6 KB runs code
- scripts/check.py 976 B runs code
- scripts/data_fetcher.py 9.1 KB runs code
- scripts/data_storage.py 12 KB runs code
- scripts/factor_calculator.py 5.3 KB runs code
- scripts/init_data.py 4.1 KB runs code
- scripts/qse.py 23 KB runs code
- scripts/quick_import.py 1.7 KB runs code
- scripts/risk_controller.py 1.8 KB runs code
- scripts/stock_selector.py 3.8 KB runs code
- SKILL-REFERENCE.md 7.8 KB
- STAGE1_COMPLETE.md 6.2 KB
- STRATEGY.md 6.4 KB
- TECHNICAL_IMPLEMENTATION.md 31 KB
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.
- 1mo ago First seen · 36 lines · 4 tokens per session scan A df572b91a241
quant-strategy-engine is a skill published in the GitHub repository xyva-yuangui/XyvaClaw (21 stars, last pushed 2mo ago), licensed MIT. It adds 4 tokens to every session and 374 once invoked, about $0.0000 per session on Opus 5.5. 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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