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投资人材料

创建和更新演示文稿、一页纸、投资人备忘录、加速器申请、财务模型及融资材料。当用户需要面向投资人的文档、预测、资金用途表、里程碑计划或需要在多个融资资产间保持内部一致性的材料时使用。

投资人材料 是什么?

Investor Materials 技能旨在帮助用户构建一致、可信且易于辩护的面向投资人的材料。它适用于创建或修订演示文稿、撰写投资人备忘录或一页纸、构建财务模型、里程碑计划或资金用途表,以及回答加速器或孵化器申请问题。核心黄金法则是所有投资人材料必须相互一致,因此要求在编写前确认单一事实来源(包括牵引力指标、定价和收入假设、融资规模和工具、资金用途、团队简介和头衔、里程碑和时间线)。工作流程包括盘点规范性事实、识别缺失假设、选择资产类型、带有明确逻辑地起草资产,以及交叉核对每个数字与事实来源。该技能提供针对演示文稿、一页纸/备忘录、财务模型和加速器申请的详细指南,并列出需避免的红旗(如不可验证的主张、模糊的市场规模估算、不一致的团队角色或头衔等)。最终交付物需通过质量关卡,确保每个数字与事实来源匹配,资金用途和收入层正确加总,假设可见且故事清晰无夸大。支持的平台包括 Claude Code、Cursor、Codex 等通用 AI Agent,但本技能作为独立指南,未直接依赖特定平台。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/investor-materials

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

投资人材料 是做什么的?

构建一致性、可信且易于辩护的面向投资人的材料。

何时激活

  • 创建或修订演示文稿
  • 撰写投资人备忘录或一页纸
  • 构建财务模型、里程碑计划或资金用途表
  • 回答加速器或孵化器申请问题
  • 围绕单个事实来源对齐多个融资文档

黄金法则

所有投资人材料必须相互一致。

在编写前创建或确认单一事实来源:

  • 牵引力指标
  • 定价和收入假设
  • 融资规模和工具
  • 资金用途
  • 团队简介和头衔
  • 里程碑和时间线

如果出现冲突的数字,在起草前停止并解决它们。

核心工作流程

  1. 盘点规范性事实
  2. 识别缺失假设
  3. 选择资产类型
  4. 带有明确逻辑地起草资产
  5. 将每个数字与事实来源交叉核对

资产指南

演示文稿

推荐流程:

  1. 公司 + 楔子
  2. 问题
  3. 解决方案
  4. 产品 / 演示
  5. 市场
  6. 商业模式
  7. 牵引力
  8. 团队
  9. 竞争 / 差异化
  10. 请求
  11. 资金用途 / 里程碑
  12. 附录

如果用户需要原生网页演示文稿,将此技能与 frontend-slides 配合使用。

一页纸 / 备忘录

  • 用一句清晰的话说明公司做什么
  • 展示为什么是现在
  • 尽早包含牵引力和证据点
  • 使请求精确
  • 保持主张易于验证

财务模型

包括:

  • 明确的假设
  • 在有用时包含悲观 / 基准 / 乐观情况
  • 清晰的逐层收入逻辑
  • 与里程碑关联的支出
  • 在决策依赖于假设时进行敏感性分析

加速器申请

  • 回答确切提出的问题
  • 优先展示牵引力、洞察力和团队优势
  • 避免夸大其词
  • 保持内部指标与演示文稿和模型一致

需避免的红旗

  • 不可验证的主张
  • 无假设的模糊市场容量估算
  • 不一致的团队角色或头衔
  • 不能正确加总的收入计算
  • 在假设脆弱之处夸大确定性

质量关卡

在交付前:

  • 每个数字都与当前事实来源匹配
  • 资金用途和收入层正确加总
  • 假设可见而非隐藏
  • 故事清晰且无夸张语言
  • 最终资产在合作伙伴会议上能够辩护

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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