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 vivy-yi/finance-skills --skill fx-forecast-analysisgit clone --depth 1 https://github.com/vivy-yi/finance-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/vivy-yi/finance-skills/fx-forecast-analysis)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/fx-forecast-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/fx-forecast-analysis/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/vivy-yi/finance-skills/fx-forecast-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/fx-forecast-analysis.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 | $0.00096 | $0.01944 |
| Opus 5 | $0.00048 | $0.00972 |
| Sonnet 5 | $0.00019 | $0.00389 |
| Haiku 4.5 | $0.00010 | $0.00194 |
Grade A, and why
fx-forecast-analysis 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 9d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
加载上下文
首次使用时: 读取 ../../CLAUDE.md 获取场景级配置(宏观因子权重/历史预测准确率/情景定义)。
/fx-forecast-analysis — 外汇预测分析
Examples
→ 示例:用户说"帮我们预测一下未来 3 个月的人民币走势,主要参考哪些因素",系统应调用本技能,执行人民币汇率预测分析。
→ 示例:用户说"汇率波动对我们的出口收入影响有多大,帮我做个敏感性分析",系统应调用本技能,分析汇率对收入的敏感性。
→ 示例:用户说"欧洲市场的收入预测需要转换成美元,帮我评估一下汇率假设",系统应调用本技能,制定汇率预测假设并评估影响。
第一步:当前汇率与历史分析
当前汇率状态:
□ 即期汇率:[USD/CNY = X.XXXX]
□ 隐含收益率(Carry):[X]%(利差收益/损失)
□ 近期走势(过去 [X] 个月):
→ 最高:[X.XXXX]
→ 最低:[X.XXXX]
→ 波动幅度:[X]%(高低点差异%)
→ 当前处于:[区间位置,高/中/低]
历史预测准确率:
□ 过去 [X] 次预测表现:
→ 预测方向正确率:[X]%
→ 平均误差:[X]%(预测 vs 实际)
→ 最大误差:[X]%
□ 预测可靠性评估:[高/中/低]
第二步:宏观因子分析
利差因素:
□ 中美利差:
→ 中国 10 年国债收益率:[X]%
→ 美国 10 年国债收益率:[X]%
→ 利差:[X]%([中国-美国])
→ 趋势:[收窄/扩大]
□ 利差对汇率影响:
→ 利差收窄 → [USD/CNY 下行/上行]
→ 历史相关性:[X]%(利差解释力)
→ 当前利差信号:[看多/看空] USD
经济基本面:
□ 中国经济:
→ GDP 增速:[X]%
→ 出口增速:[X]% — 对 USD 收入影响 [X]%
→ 贸易顺差:[X] 亿美元
□ 美国经济:
→ GDP 增速:[X]%
→ 核心 PCE:[X]%
→ 劳动力市场:[紧张/稳健/疲软]
□ 基本面评估:[中国相对走强/走弱/持平]
政策与地缘因素:
□ 货币政策:
→ 中国央行立场:[宽松/中性/紧缩]
→ 美联储立场:[宽松/中性/紧缩]
□ 政策信号评估:
→ [描述当前主要政策驱动]
□ 地缘风险:[高/中/低] — 影响 [描述]
第三步:技术面分析
技术指标:
□ 移动平均线:
→ 50 日均线:[X.XXXX] — 当前 [高于/低于]
→ 200 日均线:[X.XXXX] — 当前 [高于/低于]
→ 趋势信号:[看多/看空/中性]
□ 相对强弱指数(RSI):
→ 当前 RSI:[X]([超买/超卖/中性])
□ 布林带:
→ 当前价格:[突破上轨/在中轨附近/突破下轨]
□ 技术面综合信号:[看多/看空/中性]
第四步:情景预测
情景设计与概率:
□ 情景 1:基准情景(概率 [X]%)
→ 假设:中、美经济平稳,利差稳定
→ 3 个月目标:[X.XXXX]([+/-X]%)
→ 12 个月目标:[X.XXXX]([+/-X]%)
□ 情景 2:美元走强(概率 [X]%)
→ 假设:美联储鹰派,GDP 超预期
→ 3 个月目标:[X.XXXX]
→ 12 个月目标:[X.XXXX]
□ 情景 3:美元走弱(概率 [X]%)
→ 假设:中国经济复苏,贸易改善
→ 3 个月目标:[X.XXXX]
→ 12 个月目标:[X.XXXX]
综合预测:
□ 最可能情景:情景 [X]
□ 3 个月预测区间:[X.XXXX] - [X.XXXX]([X]% 置信区间)
□ 12 个月预测区间:[X.XXXX] - [X.XXXX]
第五步:风险管理建议
基于预测的风险敞口建议:
□ 当前敞口方向:[多头 USD/空头 USD/中性]
□ 预测建议:[加仓/减仓/维持]
□ 调整比例:[X]%
□ 止损/止盈设置:
→ 止损位:[X.XXXX](如 USD 跌破)
→ 止盈位:[X.XXXX](如 USD 触及)
第六步:生成预测报告
═══════════════════════════════════════
外汇预测分析报告
币种对:[USD/CNY]
预测日期:[YYYY-MM-DD]
预测周期:[3 个月/12 个月]
═══════════════════════════════════════
【当前汇率】
□ 即期:[X.XXXX]
□ 近期波动:[X]%([高/中/低])
【核心驱动因素】
□ 利差因素:[看多/看空] USD — 当前利差 [X]%
□ 基本面:[中国走强/走弱/持平]
□ 政策:[主要驱动描述]
□ 技术面:[看多/看空/中性]
【情景预测】
□ 情景 1(基准,[X]%):3M [X.XXXX],12M [X.XXXX]
□ 情景 2(美元走强,[X]%):3M [X.XXXX],12M [X.XXXX]
□ 情景 3(美元走弱,[X]%):3M [X.XXXX],12M [X.XXXX]
【综合预测】
□ 最可能:情景 [X]
□ 3 个月区间:[X.XXXX] - [X.XXXX]
□ 12 个月区间:[X.XXXX] - [X.XXXX]
【历史预测准确率】
□ 方向正确率:[X]% | 平均误差:[X]%
【风险管理建议】
□ 敞口建议:[调整建议]
□ 止损位:[X.XXXX] | 止盈位:[X.XXXX]
═══════════════════════════════════════
置信度:[✅ 高 / ⚠️ 中 / 🔴 低]
注:汇率预测存在不确定性,决策应结合风险承受能力
═══════════════════════════════════════
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.
- 9d ago First seen · 221 lines · 96 tokens per session scan A f5eed010946e
fx-forecast-analysis is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 96 tokens to every session and 1,944 once invoked, about $0.0005 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 2026-09-03.
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