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 scenario-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/scenario-analysis)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/scenario-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/scenario-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/scenario-analysis"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/scenario-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 | $0.00095 | $0.02597 |
| Opus 5 | $0.00048 | $0.01299 |
| Sonnet 5 | $0.00019 | $0.00519 |
| Haiku 4.5 | $0.00010 | $0.00260 |
Grade A, and why
scenario-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 11d 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 — 278 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(情景定义标准/历史基准/概率估算方法)。
/scenario-analysis — 情景分析
Examples
→ 示例:用户说"帮我做一个三种情景的财务预测:乐观/基准/悲观",系统应调用本技能,执行三情景财务预测分析。
→ 示例:用户说"原材料价格暴涨 30% 对我们明年利润的影响有多大",系统应调用本技能,执行价格冲击敏感性分析。
→ 示例:用户说"竞争者价格战开始了,帮我评估对我们的影响",系统应调用本技能,执行竞争情景分析。
第一步:分析框架确定
分析标的与范围:
□ 分析对象:[项目/业务单元/公司]
□ 分析维度:[财务回报/现金流/估值/战略影响]
□ 币种:[CNY]
□ 关键假设变量([X] 个):
→ 变量 1:[收入增长率] — 基准 [X]%
→ 变量 2:[毛利率] — 基准 [X]%
→ 变量 3:[利率/折现率] — 基准 [X]%
→ 变量 4:[...] — 基准 [...]
情景数量与定义:
□ 情景结构:
→ 情景 1:基准情景(最可能发生)
→ 情景 2:乐观情景(上行情景)
→ 情景 3:悲观情景(下行情景)
→ [情景 4:极端悲观(如需)]
→ [情景 5:极端乐观(如需)]
□ 情景概率分配:
→ 基准:[X]%
→ 乐观:[X]%
→ 悲观:[X]%
→ 合计:100%
第二步:基准情景设计
基准情景假设:
□ 宏观经济假设:
→ GDP 增速:[X]%
→ 行业增速:[X]%
→ 利率水平:[X]%
□ 业务假设:
→ 收入增长率:[X]%([假设依据])
→ 毛利率:[X]%([假设依据])
→ 运营费用率:[X]%
→ 资本支出:[X] 万/年
□ 财务预测([X] 年):
→ Year 1:收入 [X] 万,EBITDA [X] 万
→ Year 2:收入 [X] 万,EBITDA [X] 万
→ Year 3:收入 [X] 万,EBITDA [X] 万
基准情景财务结果:
□ 核心指标:
→ NPV:[X] 万 [✅ > 0 / 🔴 < 0]
→ IRR:[X]%(vs 门槛 [X]%)[✅ > 门槛 / 🔴 < 门槛]
→ 回收期:[X] 年
□ 自由现金流(FCF):
→ Year 1:[X] 万
→ Year 2:[X] 万
→ Year 3:[X] 万
→ 累计 FCF:[X] 万
第三步:乐观/悲观情景设计
乐观情景假设(较基准 [+/-X]%):
□ 假设变化:
→ 收入增长率:[X]%(基准 [X]%,提升 [X]%)
→ 毛利率:[X]%(基准 [X]%,提升 [X]%)
→ 运营费用率:[X]%(基准 [X]%,降低 [X]%)
□ 乐观情景财务结果:
→ NPV:[X] 万
→ IRR:[X]%
→ 概率:[X]%
悲观情景假设(较基准 [-X]%):
□ 假设变化:
→ 收入增长率:[X]%(基准 [X]%,下降 [X]%)
→ 毛利率:[X]%(基准 [X]%,下降 [X]%)
→ 运营费用率:[X]%(基准 [X]%,上升 [X]%)
□ 悲观情景财务结果:
→ NPV:[X] 万 [🔴 < 0]
→ IRR:[X]%([🔴 < 门槛])
→ 概率:[X]%
情景对比汇总:
| 情景 | 收入增长率 | 毛利率 | NPV(万) | IRR | 概率 |
|------|----------|--------|----------|-----|------|
| 乐观 | [X]% | [X]% | [+X] | [X]% | [X]% |
| 基准 | [X]% | [X]% | [X] | [X]% | [X]% |
| 悲观 | [X]% | [X]% | [-X] | [X]% | [X]% |
第四步:概率加权分析
期望值(概率加权 NPV)计算:
□ 期望 NPV = Σ (情景 NPV × 概率)
= ([X] 万 × [X]%) + ([X] 万 × [X]%) + ([-X] 万 × [X]%)
= [X] 万 + [X] 万 + [-X] 万
= [X] 万
□ 期望 NPV > 0:[✅ 期望值为正 / 🔴 期望值为负]
□ NPV 下行风险:
→ 悲观情景 NPV:[-X] 万
→ 概率加权下行损失:[X] 万
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.
- 11d ago First seen · 278 lines · 95 tokens per session scan A b62461ef445a
scenario-analysis is a skill published in the GitHub repository vivy-yi/finance-skills (28 stars, last pushed 2mo ago), licensed MIT. It adds 95 tokens to every session and 2,597 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 2026-08-30.
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