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投资者外联

撰写冷邮件、暖介绍片段、跟进邮件、更新邮件以及用于融资的投资者通信。当用户希望联系天使投资人、风投、战略投资者或加速器,并需要简洁、个性化、面向投资者的消息时使用。

投资者外联 是什么?

这个技能旨在帮助 AI 代理(如 Claude Code、Cursor、Codex 等)及其用户高效地与投资者进行沟通,主要应用于融资场景。它提供了一套结构化的方法,用于撰写冷邮件、暖介绍请求、会议后的跟进邮件、融资过程中的更新邮件,以及根据基金主题或合伙人契合度定制的邮件。该技能的核心原则包括个性化每一封邮件、降低请求门槛、使用具体证据而非形容词、保持简洁,并避免千篇一律的套话。它支持多种主流 AI 代理平台,通过强制检查清单(如禁止使用“我很想认识您”等空洞开场白)和质量门(确保消息真正个性化、请求明确、证据具体)来保证输出的邮件专业且直接。该技能还集成了品牌声音处理能力,可以复用预设的语音档案(如通过 `brand-voice` 技能生成),确保输出邮件在保持投资者特定结构和请求纪律的同时,与用户期望的品牌语调一致。它提供了详细的冷邮件结构、个性化信息来源、跟进节奏(默认第0天、第4-5天、第10-12天)以及暖介绍请求的简明要求。总之,这个技能通过强制规则和模板,帮助代理生成简洁、易于操作、且高度个性化的投资者外联邮件,避免了常见的不专业表达和模糊请求,从而提升融资沟通的成功率。

兼容平台~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

投资者外联 是做什么的?

撰写简短、具体且易于行动的投资者沟通内容。

何时激活

  • 编写发送给投资者的冷邮件
  • 起草暖介绍请求
  • 在会议后或未收到回复时发送跟进邮件
  • 在融资过程中撰写投资者更新邮件
  • 根据基金主题或合伙人契合度定制邮件

核心规则

  1. 个性化每一封外发邮件。
  2. 降低请求的门槛。
  3. 使用证据而非形容词。
  4. 保持简洁。
  5. 绝不发送任何可能让投资者觉得是群发的文案。

语音处理

如果用户的语音风格很重要,请先运行 brand-voice 并复用其生成的 VOICE PROFILE。 此技能应保留投资者特有的结构和请求纪律,而不是创建自己的独立语音系统。

硬性禁止

删除并重写以下任何内容:

  • "我很想与您联系"
  • "很高兴分享"
  • 没有真正关联的泛泛的赞赏基金主题
  • 模糊的创始人的形容词
  • 乞求性语言
  • 当可以直接提出请求时却使用软性结束问题

冷邮件结构

  1. 主题行:简短且具体
  2. 开头:为什么专门选择这位投资者
  3. 核心内容:公司做什么,为什么是现在,以及哪些证据有价值
  4. 请求:一个具体的下一步行动
  5. 签名:姓名、职位以及必要时的 1 个可信度锚点

个性化信息来源

参考以下一项或多项:

  • 相关的投资组合公司
  • 公开的投资主题、演讲、帖子或文章
  • 共同的联系人
  • 与投资者关注领域明显的市场或产品契合度

如果缺少该上下文,请说明草稿仍需个性化,而不是假装它已完成。

跟进节奏

默认:

  • 第 0 天:初次外联
  • 第 4 或 5 天:简短的跟进,包含一个新数据点
  • 第 10 到 12 天:最后一次跟进,并给出明确的结束信号

之后不要再继续催促,除非用户想要更长的序列。

暖介绍请求

让连接者轻松操作:

  • 解释为什么这个介绍很合适
  • 包含一段可转发的简介
  • 保持可转发的简介少于 100 字

会后更新

包含:

  • 讨论的具体事项
  • 承诺的答案或更新
  • 如果有更新的一个证据点
  • 下一步行动

质量门

在交付之前:

  • 消息确实是个性化的
  • 请求是明确的
  • 证据点是具体的
  • 已去除空洞的赞美和软性语言
  • 字数保持简洁

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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