context-building

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Company Context Builder

公司上下文构建器

One global context file per company. Every other GTM skill reads from this file for voice, value prop, ICP, win cases, proof points, and campaign learnings.

每家公司对应一个全局上下文文件。所有其他GTM技能都会从该文件中获取话术、价值主张、ICP、成功案例、证明点以及营销活动经验等信息。

Context File Location

上下文文件位置

claude-code-gtm/context/{company}_context.md

Single file per company, not per-campaign. All skills reference this path.

claude-code-gtm/context/{company}_context.md

每家公司对应单个文件，而非按营销活动拆分。所有技能均引用此路径。

Modes

模式

Mode 1: Create

模式1：创建

Use when no context file exists yet. Walk the user through each section.

Step 1: Check if

claude-code-gtm/context/{company}_context.md

exists.

Step 2: If not, ask the user for each section (one at a time or in bulk):

Section	What to ask	Example
What We Do	Product one-liner, core value prop, email-safe value prop, key lingo, key numbers	Product description + quantifiable claims
ICP	Customer profiles, company sizes, roles, geographies	Target profiles with size ranges and regions
Win Cases	Past customers, why they bought, what worked	Concrete outcomes with metrics
Proof Library	Pre-written PS sentences for emails, mapped to audience and hypothesis	Ready-to-paste proof points
Campaign History	Past campaigns: vertical, list size, reply rate, learnings	(empty on first run)
Active Hypotheses	Current working hypotheses about what resonates	Pain points validated by campaign data

Step 3: Write the file using the schema from references/context-schema.md.

Key sections to get right:

What We Do — must include:

Product one-liner
Core value prop (internal version, can use any language)
Email-safe value prop (outreach-friendly version of the value prop)
Key numbers (quantifiable claims — database size, speed benchmarks, coverage stats)
Key lingo (internal terms and definitions)

Proof Library — must include:

Full PS sentences ready to paste into emails
Each mapped to: best audience, best hypothesis, source win case
Every proof point must trace back to a real win case
Write the sentence as it would appear in the email (including "PS.")

当上下文文件尚未存在时使用此模式。引导用户完成每个章节的填写。

步骤1： 检查

claude-code-gtm/context/{company}_context.md

是否存在。

步骤2： 若不存在，逐一或批量向用户询问以下各章节的内容：

章节	需要询问的内容	示例
我们的业务	产品一句话介绍、核心价值主张、适合邮件场景的价值主张、关键术语、关键数据	产品描述+可量化的声明
ICP	客户画像、公司规模、职位角色、地域分布	包含规模范围和地域的目标客户画像
成功案例	过往客户、购买原因、有效策略	带指标的具体成果
素材库	预写的邮件PS语句，按受众和假设分类	可直接粘贴的证明点
营销活动历史	过往营销活动：垂直领域、名单规模、回复率、经验总结	（首次创建时为空）
活跃假设	当前关于客户共鸣点的有效假设	经营销活动数据验证的痛点

步骤3： 参照references/context-schema.md中的模板编写文件。

需重点完善的章节：

我们的业务 — 必须包含：

产品一句话介绍
核心价值主张（内部版本，可使用任意表述）
适合邮件场景的价值主张（对外推广适用的版本）
关键数据（可量化的声明——数据库规模、速度基准、覆盖范围统计等）
关键术语（内部术语及定义）

素材库 — 必须包含：

可直接粘贴到邮件中的完整PS语句
每条语句需关联：最佳受众、最佳假设、来源成功案例
每个证明点必须追溯到真实的成功案例
语句需以邮件中实际呈现的格式撰写（包含“PS.”）

Mode 2: Update

模式2：更新

Use when context file exists and user wants to add or modify a section.

Step 1: Read existing context file.

Step 2: Ask what to update. Common updates:

Add a new win case
Add a campaign result
Update ICP based on new learnings
Add domains to DNC
Revise or add hypotheses
Add or update proof points in the Proof Library
Update voice rules
Update key numbers (e.g., database size grew)

Step 3: Append to the relevant section. Never overwrite existing entries — add new rows to tables, new bullets to lists.

当上下文文件已存在，且用户需要添加或修改某章节内容时使用此模式。

步骤1： 读取现有上下文文件。

步骤2： 询问用户需要更新的内容。常见更新项包括：

添加新的成功案例
添加营销活动结果
根据新经验更新ICP
向DNC列表添加域名
修改或新增假设
在素材库中添加或更新证明点
更新话术规则
更新关键数据（例如：数据库规模增长）

步骤3： 追加到对应章节。切勿覆盖现有内容——可向表格中添加新行，向列表中添加新的项目符号。

Mode 3: Call Recording Capture

模式3：通话记录捕获

Use when the user pastes a call transcript or meeting notes.

Step 1: Read the transcript.

Step 2: Extract and categorize signals:

ICP signals — who was on the call, their role, company size, what they care about
Win case data — what resonated, what they said about their current workflow, pain points confirmed
Proof point candidates — specific results or quotes that could become Proof Library entries
DNC signals — any companies or domains mentioned as off-limits
Hypothesis validation — which existing hypotheses were confirmed or refuted
Voice feedback — any reaction to tone, language, or positioning that should update Voice rules

Step 3: Present extracted signals to the user for confirmation.

Step 4: Update the context file with confirmed signals.

当用户粘贴通话转录文本或会议纪要时使用此模式。

步骤1： 读取转录文本。

步骤2： 提取并分类信号：

ICP信号 — 通话参与人员、职位、公司规模、关注重点
成功案例数据 — 客户共鸣点、他们对当前工作流程的评价、已确认的痛点
潜在证明点 — 可转化为素材库条目的具体成果或引用内容
DNC信号 — 提及的任何禁止接触的公司或域名
假设验证 — 哪些现有假设被证实或推翻
话术反馈 — 任何关于语气、语言或定位的反馈，需用于更新话术规则

步骤3： 将提取的信号呈现给用户确认。

步骤4： 根据用户确认的信号更新上下文文件。

Mode 4: Feedback Loop

模式4：反馈循环

Use when importing campaign results from your email sequencer (e.g. Instantly) or manual tracking.

Step 1: Read campaign results (CSV, pasted data, or email sequencer export e.g. Instantly).

Step 2: Extract metrics:

Campaign name, vertical, list size
Open rate, reply rate, positive reply rate
Top-performing hypotheses (which P1 angles got replies)
Patterns in positive vs negative replies

Step 3: Add a new row to the

## Campaign History

table.

Step 4: Update

## Active Hypotheses

based on results:

Promote hypotheses with high reply rates to Validated
Demote hypotheses that didn't resonate to Retired
Note any new hypotheses suggested by reply patterns

Step 5: Update

## Proof Library

if campaign results surfaced new proof points:

New win cases → write new PS sentences
Existing proof points that didn't resonate → add notes or remove

当从邮件序列工具（如Instantly）或手动跟踪中导入营销活动结果时使用此模式。

步骤1： 读取营销活动结果（CSV文件、粘贴的数据或邮件序列工具导出文件，如Instantly的导出文件）。

步骤2： 提取指标：

营销活动名称、垂直领域、名单规模
打开率、回复率、正面回复率
表现最佳的假设（哪些核心角度获得了回复）
正面回复与负面回复的模式

步骤3： 在

## 营销活动历史

表格中添加新行。

步骤4： 根据结果更新

## 活跃假设

：

将高回复率的假设升级为“已验证”
将未引起共鸣的假设降级为“已淘汰”
记录回复模式提示的新假设

步骤5： 若营销活动结果产生新的证明点，更新

## 素材库

：

新成功案例→撰写新的PS语句
未引起共鸣的现有证明点→添加备注或移除

Cross-Skill References

跨技能引用

This context file is consumed by:

```
hypothesis-building
```
— reads ICP, Win Cases, and product value prop to generate pain hypotheses
```
email-prompt-building
```
— reads Voice, What We Do, Proof Library, and Active Hypotheses to build prompt templates
```
email-generation
```
— reads the prompt template (which was built from this file)
```
list-building
```
— reads ICP and Win Cases for seed companies
```
market-research
```
— reads ICP and hypotheses for research scope
```
enrichment-design
```
— reads hypotheses for segmentation column design
```
list-segmentation
```
— reads hypotheses for tiering logic
```
email-response-simulation
```
— reads Voice rules to constrain rewrites
```
campaign-sending
```
— reads DNC list for exclusions

该上下文文件供以下技能使用：

```
hypothesis-building
```
— 读取ICP、成功案例和产品价值主张以生成痛点假设
```
email-prompt-building
```
— 读取话术规则、我们的业务、素材库和活跃假设以构建提示模板
```
email-generation
```
— 读取基于此文件构建的提示模板
```
list-building
```
— 读取ICP和成功案例以获取种子公司
```
market-research
```
— 读取ICP和假设以确定研究范围
```
enrichment-design
```
— 读取假设以设计细分列
```
list-segmentation
```
— 读取假设以制定分层逻辑
```
email-response-simulation
```
— 读取话术规则以约束改写内容
```
campaign-sending
```
— 读取DNC列表以进行排除筛选

Reference

参考文档

See references/context-schema.md for the full file schema with all sections and field definitions.

完整的文件模板及所有章节和字段定义请参见references/context-schema.md。