earnings-call-preparation

earnings-call-preparation is a skill for Claude Code from vivy-yi/finance-skills. It costs 93 tokens per session (2,039 once invoked), scanned A, original, MIT.

A workflow for preparing quarterly or annual earnings calls, where a company explains its financial results to investors and analysts. It covers the announcement, presentation, speaker notes, questions, and meeting process.

In plain words
What is it for?
It helps update results presentations, write news releases and CFO remarks, prepare analyst Q&A, explain strategy changes, and manage the call agenda.
Why use it?
Earnings calls require consistent financial information and prepared answers to difficult questions. This process helps the team organise the material and rehearse likely discussions.

Skill for Claude Code

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

Good fit It helps update results presentations, write news releases and CFO remarks, prepare analyst Q&A, explain strategy changes, and manage the call agenda.

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Install with agentmods
npx agentmods add skills/vivy-yi/finance-skills/earnings-call-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 earnings-call-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 earnings-call-preparation

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

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Your own site · 80×15
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/earnings-call-preparation"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/earnings-call-preparation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,039 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 $0.00093 $0.02039
Opus 5 $0.00046 $0.01019
Sonnet 5 $0.00019 $0.00408
Haiku 4.5 $0.00009 $0.00204

Measured 9d ago against content hash 5f7d484ddbda, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

earnings-call-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/earnings-call-preparation/SKILL.md · 242 lines

How it starts

The opening of the file, as written. The whole thing — 242 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(IR 规范/发布流程/历史 Q&A)。


/earnings-call-preparation — 业绩发布会准备

Examples

→ 示例:用户说"季度业绩发布后要做 earnings call,帮我准备 CFO 发言稿",系统应调用本技能,生成业绩电话会 CFO 发言稿。

→ 示例:用户说"帮我们预测一下分析师在 earnings call 上可能问的问题",系统应调用本技能,预测分析师问题并准备答复要点。

→ 示例:用户说"这次 earnings call 要宣布一个新战略,需要配合的材料",系统应调用本技能,准备新战略公告的 IR 材料。

第一步:业绩确认与材料更新

业绩数据确认:

□ 业绩期间:[YYYY-QX / YYYY 年度]
□ 业绩数据截止:[YYYY-MM-DD]
□ 数据状态:[✅ 已审计/已审核 / ⚠️ 初稿]

□ 核心业绩数据:
  → 收入:[X] 万(vs 预期 [+/-X]%)
  → EBITDA:[X] 万(vs 预期 [+/-X]%)
  → 净利润:[X] 万(vs 预期 [+/-X]%)
  → EPS:[X] 元(vs 预期 [+/-X]%)

□ 业绩表现判断:[超预期 / 符合预期 / 低于预期]

演示材料更新:

□ 演示文稿(PPT)页数:[X] 页
□ 须更新内容:
  → 财务业绩数据:[✅ 已更新 / ⏳ 待更新]
  → 运营 KPI:[✅ 已更新 / ⏳ 待更新]
  → 行业与市场数据:[✅ 已更新 / ⏳ 待更新]
  → 展望与指引:[✅ 已更新 / ⏳ 待更新]

□ 数据核实状态:
  → 财务数据:[✅ 已核实]
  → 运营数据:[✅ 已核实]

第二步:演示材料框架

演示材料结构(参考):

1. 业绩亮点([X] 页)
   → 一句话核心信息
   → 关键业绩指标(3-5 个)

2. 财务业绩([X] 页)
   → 收入/利润/现金流
   → 同比/环比变化

3. 业务表现([X] 页)
   → 各业务线表现
   → 运营 KPIs

4. 战略进展([X] 页)
   → 战略目标达成
   → 重点举措进展

5. 下期展望([X] 页)
   → 下期指引
   → 全年目标更新(如有)

叙事逻辑设计:

□ 整体叙事主线:"[一句话,如'收入超预期增长,盈利能力持续改善']"
□ 叙事与数据的一致性:[✅ 一致 / ⚠️ 需调整]

第三步:新闻稿撰写

新闻稿结构:

□ 新闻稿篇幅:约 [X] 字
□ 新闻稿结构:
  → 标题:[简洁有力的标题]
  → 副标题:[一句话亮点]
  → 第一段(Lead):[核心数据 + 亮点]
  → 第二段(业绩详情):[数据展开]
  → 第三段(业务亮点):[2-3 个亮点]
  → 第四段(展望):[下期指引/全年目标]
  → 高管引言:[CEO/CFO 引言]
  → 附录:关键数据表

新闻稿审核:

□ 法律合规审核:[✅ 已审核 / ⏳ 待审核]
□ 监管格式检查:[✅ 通过 / ⏳ 待检查]
□ 高管引言审核:[✅ 已确认 / ⏳ 待确认]

第四步:Q&A 准备

投资者关心问题预测:

□ 业绩相关问题:
  → Q1:[问题,如"Q3 毛利率下降的原因?" ]
  → 建议回答要点:[X] 点

  → Q2:[问题,如"下期指引下调/上调的原因?" ]
  → 建议回答要点:[X] 点

□ 业务相关问题:
  → Q1:[问题]
  → 建议回答要点:[X] 点

□ 财务相关问题:
  → Q1:[问题]
  → 建议回答要点:[X] 点

□ ESG/其他问题(如适用):
  → Q1:[问题]
  → 建议回答要点:[X] 点

敏感问题应对预案:

□ 敏感问题 1:[问题]
  → 可能被追问的场景:[描述]
  → 建议话术:"[核心回应]"
  → 可透露的额外信息(选择性):[信息]

□ 禁止事项:
  → 不得提供未经审计的详细数据
  → 不得对下期业绩做具体数字承诺
  → 不得承认存在未披露的重大风险

第五步:会议流程管理

业绩发布会流程:

□ 会议时间:[YYYY-MM-DD HH:MM]
□ 会议时长:约 [X] 分钟
□ 会议形式:[电话会议/视频会议/线下+线上]

□ 流程安排:
  → 管理层发言:[X] 分钟
  → 业绩演示:[X] 分钟
  → Q&A 环节:[X] 分钟

□ 参会管理层:
  → 主讲人:[姓名]([职位])
  → 问答参与:[姓名]([职位])

Read the full file on GitHub · 242 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 · 242 lines · 93 tokens per session scan A 5f7d484ddbda

Subscribe to this mod's changes

earnings-call-preparation is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 93 tokens to every session and 2,039 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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