roadshow-material-preparation

roadshow-material-preparation is a skill for Claude Code from vivy-yi/finance-skills. It costs 97 tokens per session (2,026 once invoked), scanned A, original, MIT.

A workflow for preparing materials for an investor roadshow, a series of meetings where a company presents its business and financial outlook to investors. It covers the presentation structure, data, investor questions, and rehearsal.

In plain words
What is it for?
It helps create company introductions, investment highlights, financial slides, growth plans, IPO or fundraising materials, investor Q&A, and presentation practice.
Why use it?
Roadshow information must be clear, current, and suited to the intended audience. This process helps organise the story and verify the supporting financial, operational, industry, and competitor data.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md.

Good fit It helps create company introductions, investment highlights, financial slides, growth plans, IPO or fundraising materials, investor Q&A, and presentation practice.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vivy-yi/finance-skills/roadshow-material-preparation
Install

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.

Any agent
npx skills add vivy-yi/finance-skills --skill roadshow-material-preparation
Clone the repo
git clone --depth 1 https://github.com/vivy-yi/finance-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for roadshow-material-preparation

README.md
[![agentmods](https://agentmods.dev/badge/skills/vivy-yi/finance-skills/roadshow-material-preparation/github.svg)](https://agentmods.dev/skills/vivy-yi/finance-skills/roadshow-material-preparation)
Your own site
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/roadshow-material-preparation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/roadshow-material-preparation/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.

agentmods 80×15 button for roadshow-material-preparation

Your own site · 80×15
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/roadshow-material-preparation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/roadshow-material-preparation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,026 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00097 $0.02026
Opus 5 $0.00048 $0.01013
Sonnet 5 $0.00019 $0.00405
Haiku 4.5 $0.00010 $0.00203

Measured 9d ago against content hash e34d0fa4bf20, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

roadshow-material-preparation 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.

finance-skills/skills/investor-relations/skills/roadshow-material-preparation/SKILL.md · 252 lines

How it starts

The opening of the file, as written. The whole thing — 252 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(路演规范/披露标准/竞品信息)。


/roadshow-material-preparation — 路演材料准备

Examples

→ 示例:用户说"下周到香港和新加坡路演,帮我们准备一套核心材料",系统应调用本技能,生成路演核心材料包。

→ 示例:用户说"这次路演要给机构投资者讲 M&A 战略,需要一套估值材料",系统应调用本技能,准备 M&A 战略路演材料。

→ 示例:用户说"帮我们准备一套 20 页的 NDR 材料,针对长期投资者",系统应调用本技能,生成 NDR 投资者材料。

第一步:路演范围确认

路演基本信息:

□ 路演类型:[IPO/增发/年度投资者日/反向路演]
□ 路演目标受众:[机构投资者/零售投资者/混合]
□ 路演时间:[YYYY-MM-DD 至 YYYY-MM-DD]
□ 路演地点:[城市/线上/混合]
□ 目标投资者数量:[X] 家

□ 核心信息须传递:
  → [核心信息1]
  → [核心信息2]

第二步:材料框架设计

路演材料结构:

□ 标准路演材料框架([X] 页):

1. 公司概览([X] 页)
   → 业务模式简介
   → 发展历程与里程碑
   → 愿景与战略

2. 投资亮点([X] 页)
   → 亮点 1:[数据支持]
   → 亮点 2:[数据支持]
   → 亮点 3:[数据支持]

3. 行业与竞争([X] 页)
   → 行业规模与增速
   → 竞争格局与差异化优势
   → 市场渗透率 vs 空间

4. 财务业绩([X] 页)
   → 收入/利润/现金流趋势
   → 盈利能力分析
   → 财务健康度指标

5. 增长策略([X] 页)
   → 短期(1-2 年)执行计划
   → 中期(3-5 年)战略方向
   → 长期愿景

6. 附录([X] 页)
   → 关键术语定义
   → 财务数据明细

每页设计规范:

□ 标题规范:"[一句话核心信息]"
□ 数据密度:每页 [X]-[X] 个关键数据点
□ 图表 vs 文字比例:[X]:[X](图表为主)
□ 每页建议字数:≤ [X] 字

第三步:材料内容准备

核心数据更新:

□ 路演数据截止:[YYYY-MM-DD]
□ 须更新的数据:
  → 财务数据:[最新季度/年度]
  → 运营数据:[最新 KPI]
  → 行业数据:[最新市场规模/增速]
  → 竞品数据:[最新市场份额变化]

□ 数据核实状态:
  → 财务数据:[✅ 已核实 / ⚠️ 待核实]
  → 运营数据:[✅ 已核实 / ⚠️ 待核实]
  → 行业数据:[✅ 已核实 / ⚠️ 待核实]

投资亮点撰写:

□ 亮点 1:[标题,如"行业领先的盈利能力"]
  → 一句话总结:"[一句话]"
  → 数据支持:[X] 亿收入,[X]% 净利率
  → 与行业对比:净利率高于行业中位数 [X] 个百分点
  → 持续性说明:[壁垒描述]

□ 亮点 2:[...]

第四步:Q&A 准备

常见问题清单:

□ 业务类问题:
  → Q1:[问题] — 建议回答:[回答要点]
  → Q2:[问题] — 建议回答:[回答要点]

□ 财务类问题:
  → Q1:[问题] — 建议回答:[回答要点]

□ 竞争类问题:
  → Q1:[问题] — 建议回答:[回答要点]

□ 增长类问题:
  → Q1:[问题] — 建议回答:[回答要点]

敏感问题应对:

□ 敏感问题 1:[问题]
  → 可能意图:[质疑/试探/攻击]
  → 建议回应策略:[承认+改善/解释+转移/防守]
  → 标准回答:"[建议话术]"

□ 敏感问题 2:[问题]
  → ...

风险披露准备:

□ 须主动披露的风险:
  → 风险 1:[描述] — 应对 [措施]
  → 风险 2:[描述]

□ 禁止事项:
  → 不得承诺未经审计的财务数据
  → 不得披露未公开的重大信息
  → 不得对竞争对手进行未经证实的负面评价

第五步:演练准备

路演练排:

□ 演练时间:[预演日期]
□ 演练参与人:[高管名单]
□ 演练内容:
  → 演讲([X] 分钟)
  → Q&A 模拟([X] 分钟)

□ 演练问题记录:
  → 问题 1:[在演练中暴露的弱点] — 改进措施 [描述]
  → 问题 2:[...]

Read the full file on GitHub · 252 lines

Changes

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.

  1. 9d ago First seen · 252 lines · 97 tokens per session scan A e34d0fa4bf20

Subscribe to this mod's changes

roadshow-material-preparation is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 97 tokens to every session and 2,026 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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