everything-claude-code-zh is a Chinese translation of a collection of configurations for Claude Code and other AI coding agents. It provides agents, skills, hooks, commands, rules, and MCP configurations intended to support development workflows such as memory persistence, security scanning, evaluation, and research-first work. The catalogue includes commands, skills, agents, instructions, and a plugin from this configuration set.
Borrowing it
Nothing to install: this file belongs to xu-xiang/everything-claude-code-zh. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/xu-xiang/everything-claude-code-zh/main/.agents/skills/investor-outreach/SKILL.mdgit clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xu-xiang/everything-claude-code-zh/investor-outreach)<a href="https://agentmods.dev/skills/xu-xiang/everything-claude-code-zh/investor-outreach"><img src="https://agentmods.dev/badge/skills/xu-xiang/everything-claude-code-zh/investor-outreach/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xu-xiang/everything-claude-code-zh/investor-outreach"><img src="https://agentmods.dev/badge/skills/xu-xiang/everything-claude-code-zh/investor-outreach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00757 |
| Opus 5 | $0.00027 | $0.00378 |
| Sonnet 5 | $0.00011 | $0.00151 |
| Haiku 4.5 | $0.00005 | $0.00076 |
Grade A, and why
investor-outreach scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
投资者触达（Investor Outreach）
编写简短、个性化且易于跟进的投资者沟通邮件。
激活时机（When to Activate）
- 向投资者撰写冷启动邮件（Cold Email）时
- 起草请求引荐（Warm Intro）的说明时
- 在会议后或未收到回复时发送跟进邮件（Follow-ups）时
- 在融资过程中撰写投资者进展更新（Investor Updates）时
- 根据基金投资策略（Thesis）或合伙人契合度定制触达内容时
核心原则（Core Rules）
- 个性化：每一条发出的消息都必须经过个性化定制。
- 降低门槛：让对方的行动诉求（Ask）保持低摩擦。
- 数据说话：使用事实和证明（Proof），而非形容词。
- 保持简洁：言简意赅。
- 拒绝群发：严禁发送可以发给任何投资者的通用文案。
冷启动邮件结构（Cold Email Structure）
- 邮件主题：简短且具体。
- 开场白：为什么要特别联系这位投资者。
- 推介（Pitch）：公司业务、时机选择（Why Now）、核心数据/证明。
- 诉求（Ask）：一个具体的下一步行动。
- 落款：姓名、职位，必要时附带一个可信度背书（Credibility Anchor）。
个性化来源（Personalization Sources）
参考以下一项或多项内容：
- 相关的投资组合公司（Portfolio Companies）。
- 公开的投资策略、演讲、文章或帖子。
- 共同的联系人。
- 市场或产品与投资者关注重点的明确契合点。
如果缺少这些上下文，请向用户索取，或说明该草案是一个等待个性化的模板。
跟进节奏（Follow-Up Cadence）
默认策略：
- 第 0 天：初次触达。
- 第 4-5 天：简短跟进，并提供一个新数据点。
- 第 10-12 天：最后一次跟进，并得体地结束。
除非用户要求更长的序列，否则在此之后不要继续催促。
引荐请求（Warm Intro Requests）
为引荐人提供便利：
- 说明为什么这次引荐是契合的。
- 包含一段可直接转发的简介（Forwardable Blurb）。
- 转发简介字数保持在 100 字以内。
会后更新（Post-Meeting Updates）
应包含：
- 讨论的具体事项。
- 承诺的回答或更新。
- 一个可用的新证明点（如有）。
- 下一步计划。
质量把控（Quality Gate）
在交付前自检：
- 消息是否已个性化？
- 诉求（Ask）是否明确？
- 是否包含废话或乞求式语言？
- 证明点（Proof Point）是否具体？
- 字数是否精简？
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 77 lines · 54 tokens per session scan A dc1bca98bcaa
investor-outreach is a skill published in the GitHub repository xu-xiang/everything-claude-code-zh (1,938 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 757 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
investor-materials
Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.
dcf-model
Build discounted cash flow valuation workbooks in Excel.
comps-analysis
Build comparable-company valuation workbooks in Excel.
mle-workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
autonomous-loops
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
django-patterns
Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps.