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投資者拓展

撰寫募資用的冷郵件、溫暖介紹短文、後續追蹤、更新郵件以及投資者溝通信件。當使用者需要與天使投資人、創投、策略投資人或加速器溝通,並需要簡潔、個人化、面向投資者的訊息時使用。

投資者拓展 是什麼?

此技能專為需要進行募資溝通的使用者設計,它能協助撰寫各種面向投資者的訊息,包括冷郵件、溫暖介紹請求、後續追蹤信、投資者更新郵件等。它的核心目標是產出簡短、具體且易於採取行動的內容,避免常見的模糊用語與無效讚美。 此技能支援多種溝通場景,並提供嚴格的品質把關,確保每則訊息都經過個人化處理、明確提出請求、並有具體佐證。它會特別提醒使用者從相關投資組合公司、公開論述、共同人脈或市場契合度等來源進行個人化,若缺乏相關資訊,則會明確指出草稿仍需個人化。 此外,技能也定義了預設的後續追蹤節奏(第0天、第4-5天、第10-12天),並規範了溫暖介紹請求的格式,讓推薦人能輕鬆轉發。最後,它會強制執行「品質閘門」,確保在交付前訊息已滿足個人化、請求明確、證據具體、無冗詞贅語等條件。

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

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

投資者拓展 是做什麼的?

撰寫簡短、具體且易於採取行動的投資者溝通內容。

何時啟用

  • 撰寫給投資者的冷郵件
  • 草擬溫暖介紹請求
  • 在會議後或未獲回覆時發送後續追蹤
  • 在募資過程中撰寫投資者更新
  • 根據基金投資論點或合夥人契合度調整拓展內容

核心規則

  1. 每則對外訊息都須個人化。
  2. 讓請求門檻盡可能低。
  3. 使用證據而非形容詞。
  4. 保持簡潔。
  5. 絕不發送可以寄給任何投資者的通用副本。

語氣處理

如果使用者的語氣很重要,請先執行 brand-voice 並重複使用其 VOICE PROFILE。 此技能應保持投資者專屬的結構和請求紀律,而非建立自己的平行語氣系統。

嚴格禁止

刪除並改寫以下任何內容:

  • 「我很樂於聯繫」
  • 「很興奮分享」
  • 沒有實際關聯性的泛泛論點讚美
  • 模糊的創辦人形容詞
  • 懇求式的語言
  • 當直接請求更清晰時,使用軟性的結尾提問

冷郵件結構

  1. 主旨行:簡短且具體
  2. 開場:為何特別選擇這位投資者
  3. 提案:公司做什麼、為何是現在、以及何種證據有意義
  4. 請求:一個具體的下一個步驟
  5. 結尾:姓名、職位,以及一個必要的可信度錨點

個人化來源

參考以下一項或多項:

  • 相關的投資組合公司
  • 公開的投資論點、演講、貼文或文章
  • 共同人脈
  • 與投資者關注領域的明確市場或產品契合度

如果缺少這些背景資訊,請說明草稿仍需個人化,而非假裝已完成。

後續追蹤節奏

預設:

  • 第 0 天:首次對外聯繫
  • 第 4 或 5 天:簡短後續,附上一個新數據點
  • 第 10 至 12 天:最終後續,帶有明確的收尾

除非使用者想要更長的序列,否則之後不要再持續提醒。

溫暖介紹請求

讓推薦人輕鬆行事:

  • 解釋為何此介紹是合適的
  • 包含一封可轉發的短文
  • 將可轉發的短文控制在 100 字以內

會後更新

包含:

  • 具體討論的事項
  • 承諾的答案或更新
  • 一個新的佐證點(如有)
  • 下一個步驟

品質閘門

在交付前:

  • 訊息確實經過個人化
  • 請求是明確的
  • 佐證點是具體的
  • 多餘的讚美和軟化語言已移除
  • 字數保持精簡

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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