Communitygithub.com

投资者材料

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

投资者材料 是什么?

该技能专为需要制作和更新投资者材料的开发者或AI代理用户设计,确保所有文档(如演示文稿、一页纸、投资者备忘录、财务模型和加速器申请)在逻辑和数字上保持一致。它通过建立单一事实来源(包括牵引指标、定价假设、融资规模和资金用途)来避免冲突,从而创建可信且易于辩护的资产。支持平台包括通用AI代理,但可与前端幻灯片技能配合生成网页版演示文稿。核心工作流程包括盘点事实、识别缺失假设、选择资产类型、起草文档并交叉验证所有数字。该技能强调避免不可验证的主张、模糊市场测算和不一致的团队角色,并在交付前指导用户检查每个数字是否与事实来源匹配、资金用途是否正确求和、假设是否明确,确保材料在合作伙伴会议中经得起推敲。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/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

投资者材料 是做什么的?

构建一致、可信且易于辩护的面向投资者的材料。

何时激活

  • 正在创建或修改演示文稿
  • 撰写投资者备忘录或一页纸
  • 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
  • 围绕单一事实来源对齐多个融资文档

黄金法则

所有投资者材料必须彼此一致。

在撰写前创建或确认单一事实来源:

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

如果出现冲突的数字,请停下来解决它们,然后再起草。

核心工作流程

  1. 盘点规范事实
  2. 识别缺失假设
  3. 选择资产类型
  4. 使用明确的逻辑起草资产
  5. 对照事实来源交叉检查每个数字

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
  8. 团队
  9. 竞争 / 差异化
  10. 融资要求
  11. 资金用途 / 里程碑
  12. 附录

如果用户想要一个网页原生的演示文稿,请将此技能与 frontend-slides 配对。

一页纸 / 备忘录

  • 用一句简洁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明点
  • 精确说明融资要求
  • 保持声明易于验证

财务模型

包括:

  • 明确的假设
  • 在有用时包含悲观 / 基准 / 乐观情景
  • 清晰的分层收入逻辑
  • 与里程碑相关的支出
  • 在决策依赖于假设时进行敏感性分析

加速器申请

  • 回答被问到的确切问题
  • 优先考虑牵引力、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

应避免的危险信号

  • 不可验证的主张
  • 没有假设的模糊市场测算
  • 不一致的团队角色或头衔
  • 收入计算无法整洁求和
  • 在假设脆弱的地方过度自信

质量门

在交付前:

  • 每个数字与当前的事实来源匹配
  • 资金用途和收入层次正确求和
  • 假设是可见的,而不是隐藏的
  • 故事清晰,没有夸张语言
  • 最终资产在合作伙伴会议中经得起辩护

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

相关技能