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投資者資料生成

建立和更新簡報、一頁摘要、投資者備忘錄、加速器申請書、財務模型和募資文件。當使用者需要投資者導向文件、預測、資金用途表、里程碑計畫或須在多個募資產出間保持內部一致性的資料時使用。

投資者資料生成 是什麼?

此技能旨在協助開發者和創辦人生產專業、一致且可防禦的投資者導向資料。它能生成或更新簡報、一頁摘要、投資者備忘錄、加速器申請書、財務模型與募資相關文件。技能的核心優勢在於強制所有文件共用一個「單一事實來源」——即統一的假設數據(如營收、團隊、資金用途),並在生成前進行交叉驗證,避免數據矛盾。支援的平台包括任何以Markdown為基礎的AI代理(如Claude Code、Cursor)。使用時,AI會先盤點既有事實、識別遺漏假設、選擇資產類型,然後以明確的邏輯草擬文件,最後逐項與事實來源比對,確保數字一致。此技能還內建品質檢查與紅旗警示,例如避免不可驗證的聲稱、模糊市場規模、不一致的團隊角色或加總錯誤的營收計算,最終產出可在合作夥伴會議中站得住腳的專業文件。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

投資者資料

建立對投資者來說一致、可信且易於辯護的文件。

何時啟用

  • 建立或修改簡報
  • 撰寫投資者備忘錄或一頁摘要
  • 建立財務模型、里程碑計畫或資金用途表
  • 回答加速器或孵化器申請問題
  • 圍繞單一事實來源對齊多份募資文件

黃金法則

所有投資者資料必須彼此一致。

在撰寫之前,建立或確認一個單一事實來源:

  • 成長指標
  • 定價和營收假設
  • 募資金額與工具
  • 資金用途
  • 團隊簡介與頭銜
  • 里程碑和時間線

如果出現衝突的數字,請先停下並解決它們,再繼續起草。

核心工作流程

  1. 盤點規範性的事實
  2. 識別缺失的假設
  3. 選擇資產類型
  4. 以明確的邏輯草擬資產
  5. 將每個數字與事實來源交叉比對

資產指引

簡報

推薦流程:

  1. 公司 + 切入點
  2. 問題
  3. 解決方案
  4. 產品 / 示範
  5. 市場
  6. 商業模式
  7. 成長指標
  8. 團隊
  9. 競爭 / 差異化
  10. 募資需求
  11. 資金用途 / 里程碑
  12. 附錄

如果使用者想要網頁原生的簡報,請將此技能與 frontend-slides 搭配使用。

一頁摘要 / 備忘錄

  • 用一句簡潔的句子說明公司做什麼
  • 展示為什麼是現在
  • 早期包含成長指標和證明點
  • 明確提出募資需求
  • 保持聲明易於驗證

財務模型

包含:

  • 明確的假設
  • 在有用時包含保守 / 基準 / 樂觀三種情境
  • 乾淨的層次化營收邏輯
  • 與里程碑相關的支出
  • 敏感度分析(當決策取決於假設時)

加速器申請

  • 回答所問的具體問題
  • 優先呈現成長指標、洞察和團隊優勢
  • 避免浮誇言辭
  • 保持內部指標與簡報和模型一致

要避免的紅旗

  • 不可驗證的聲明
  • 沒有假設的模糊市場規模
  • 不一致的團隊角色或頭銜
  • 無法乾淨加總的營收計算
  • 在假設脆弱的地方過度確定

品質檢查

在交付前:

  • 每個數字都與當前的事實來源相符
  • 資金用途和營收層次正確加總
  • 假設可見,而非隱藏
  • 故事清晰,無誇張語言
  • 最終資產可在合作夥伴會議中經得起辯護

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.

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