Email Prompt Building

Overview

Generate self-contained email prompt templates for cold outreach campaigns. Reads from the company context file (voice, value prop, proof points) and campaign research (hypotheses, data points) to produce a prompt that the email-generation skill runs per-row against a contact CSV. One prompt per campaign.

When to Use

Trigger this skill when you need to:

Create a new cold email campaign
Build an outreach prompt for a new vertical
Draft email templates with research data
Set up an email sequence

Trigger phrases: “cold email”, “outreach prompt”, “email campaign”, “new vertical email”, “draft email prompt”, “email sequence”

Architectural Principle

This skill is a generator, not a template. It reads the company context file and campaign research, reasons about what fits this specific audience, and produces a self-contained prompt. Each campaign gets its own prompt. Each company gets its own context file. Nothing is hardcoded in this skill.

                      BUILD TIME (this skill)
                      ┌─────────────────────────────────────┐
context file ────────▶│                                     │
research / hypothesis ▶│  Synthesize into self-contained     │──▶ prompt template (.md)
enrichment columns ───▶│  prompt with reasoning baked in     │
                      └─────────────────────────────────────┘

                      RUN TIME (email-generation skill)
                      ┌─────────────────────────────────────┐
prompt template (.md) ▶│                                     │
contact CSV ──────────▶│  Generate emails per row            │──▶ emails CSV
                      └─────────────────────────────────────┘

What This Skill Reads

Input	Source	What to Extract
Context file	`claude-code-gtm/context/{company}_context.md`	Voice, sender, value prop, proof library, key numbers, banned words
Research	`claude-code-gtm/context/{vertical-slug}/sourcing_research.md`	Verified data points, statistics, tool comparisons
Hypothesis set	`claude-code-gtm/context/{vertical-slug}/hypothesis_set.md`	Numbered hypotheses with mechanisms and evidence
Enrichment columns	CSV headers from list-enrichment output	Field names and what they contain
Campaign brief	User describes audience, roles, goals	Target vertical, role types, campaign angle

What This Skill Produces

A single .md file at claude-code-gtm/prompts/{vertical-slug}/en_first_email.md containing:

Role line — who the LLM acts as (from context file → Voice → Sender)
Core pain — why this audience has this problem (from research, not generic)
Voice rules — tone, constraints, banned words (from context file → Voice)
Research context — verified data points embedded directly
Enrichment data fields — table mapping each CSV column to how to use it
Hypothesis-based P1 rules — rich descriptions with research data, mechanisms, evidence
P2 value angle — synthesized from context file → What We Do, adapted per hypothesis
P3 CTA rules — campaign-specific examples
P4 proof points — selected from context file → Proof Library, with conditions for when to use each
Output format — JSON keys, word limits
Banned phrasing — from context file → Voice → Banned words + campaign-specific additions

Building a Campaign Prompt

Read Upstream Data

Read these files before writing anything:

claude-code-gtm/context/{company}_context.md
claude-code-gtm/context/{vertical-slug}/sourcing_research.md
claude-code-gtm/context/{vertical-slug}/hypothesis_set.md

Synthesize (The Reasoning Step)

This is where the skill does real work. For each section of the prompt:

Voice → from context file

Read ## Voice section
Copy sender name, tone, constraints, banned words into the prompt
Do NOT invent voice rules. If the context file doesn’t have them, ask the user.

P1 → from research + hypotheses

For each hypothesis in the campaign:

Write a rich description using data points from the research
Explain the MECHANISM (why this pain exists), not just the symptom
Include specific numbers from the research (coverage percentages, decay rates, time costs)
Write P1 rules that reference enrichment fields by name
NEVER use generic framing like “scores suppliers” or “manages vendors.” Use the platform_type enrichment field or derive the actual description from the company profile.

Competitive Awareness Rules

If enrichment data or research reveals the prospect has an existing capability that overlaps:

NEVER pitch as a replacement. Position as a data layer underneath.
Acknowledge their existing tool by name in P1.
Shift P2 from “here’s what we do” to “here’s what we add to what you already do.”
If the prospect FOUNDED a competing product (career history):
- Use Variant D (peer founder) and reference shared context, OR
- Deprioritize. Flag to user as “risky send, needs manual review.”

P2 → from context file → What We Do

Read the product description, email-safe value prop, and key numbers
Reason about which value angle matters for THIS audience and THIS hypothesis
Write 2-3 hypothesis-matched value angles with the reasoning embedded
Use the email-safe value prop, not the raw version (avoid banned words)

Example query rules:

The example query MUST reference a vertical or category the prospect’s platform actually serves
NEVER reuse the same example query across different prospects
Format: “{category} in {geography} under {size constraint}”

P4 → from context file → Proof Library

Select proof points based on THREE dimensions:

Dimension	Logic
Peer relevance	Proof company should be same size or larger than prospect. Never cite a smaller company as proof to a bigger one.
Hypothesis alignment	Proof point should validate the same hypothesis used in P1.
Non-redundancy	If a stat appears in P2, do NOT repeat it in P4.

If no proof point meets all three criteria, drop P4 entirely (use a shorter structural variant instead).

Banned Phrasing

Start with banned words from context file → Voice
Add any campaign-specific banned phrases discovered during generation or email-response-simulation

Assemble the Prompt

Write the .md file following this skeleton:

[Role line from context → Voice → Sender]

[Core pain — 2-3 sentences from research. Not generic.]

## Hard constraints
[From context → Voice. Copied verbatim.]

## Research context
[Verified data points from sourcing_research.md. Actual numbers, tool names,
coverage gaps. This is the foundation for P1.]

## Enrichment data fields
[Table: field name → what it tells you → how to use it in the email]

## Hypothesis-based P1
[Per hypothesis: mechanism, evidence, usage rules.
All grounded in research data.]

## Role-based emphasis
[Map role keywords → emphasis. Use specific data points.]

## Structural variants
[Select variant per recipient based on role + seniority from enrichment data.]

## Competitive awareness
[Rules for handling prospects with overlapping capabilities.]

## Proof point selection
[Three-dimensional selection: peer relevance, hypothesis alignment, non-redundancy.]

## Example query rules
[Must reference prospect's actual vertical. Never reuse across prospects.]

P1 — [Rules referencing hypotheses and enrichment fields]
P2 — [Synthesized value angles per hypothesis. Key numbers from context.]
P3 — [CTA rules with campaign-specific examples]
P4 — [Proof points with conditions. Drop if no proof meets all three criteria.]

## Output format
[JSON keys]

## Banned phrasing
[From context → Voice + campaign additions]

Self-Containment Check

Before saving, verify:

Voice rules come from context file, not hardcoded in this skill
Structural variants are defined with role-based selection logic
P1 uses actual platform description, not generic framing
P2 example queries reference the prospect’s actual vertical
P4 proof points pass all three selection criteria
Competitive awareness rules are included
Research data is embedded with actual numbers
No references to external files — the email-generation skill only needs this prompt + CSV
Banned words from context file are included

Save

Save prompt templates to:

claude-code-gtm/prompts/{vertical-slug}/en_first_email.md
claude-code-gtm/prompts/{vertical-slug}/en_follow_up_email.md  (if follow-up needed)

Structural Variants

Select structure based on role + seniority from enrichment data. These are defaults. Override from context file or user input.

Variant	Who	Paragraphs	Max Words	Notes
A: Technical Evaluator	CTO, VP Eng, Head of Data	4 (P1-P4)	120	Full structure with proof point PS
B: Founder / CEO	Small company (less than 50 people)	3 (P1-P3)	90	Merge P2+P4, no separate PS
C: Executive / Board	Chairman, board member, delegates decisions	2-3	70	Forwardable, one sharp observation
D: Peer Founder	Built something adjacent or competing	2	60	Peer-to-peer tone, no product pitch

Variant Details

Variant A: Technical Evaluator

Recipients: CTO, VP Eng, Head of DataStructure: 4 paragraphs, ≤120 words

P1: Pain with concrete data point
P2: Product specs (API-first, pricing model, integration)
P3: Low-effort CTA (sample search, not a meeting)
P4: Peer proof point (PS)

Variant B: Founder / CEO

Recipients: Founder / CEO at small company (less than 50 people)Structure: 3 paragraphs, ≤90 words. No PS.

P1: Pain tied to their specific stage or market move
P2: Value + proof in one paragraph (merge P2+P4)
P3: CTA

Variant C: Executive / Chairman / Board

Recipients: Delegates decisionsStructure: 2-3 paragraphs, ≤70 words. Forwardable.

P1: One sharp observation about their platform
P2: One sentence value + CTA combined
Optional P3: Proof point only if it’s a name they’d recognize

Variant D: Peer Founder

Recipients: Built something adjacent or competingStructure: 2 paragraphs, ≤60 words. Peer-to-peer tone.

P1: Acknowledge shared context, state the angle without explaining basics
P2: Specific offer, no product pitch

Follow-up Email

2 paragraphs, ≤60 words total
P1: Case study + capability + example
P2: Sector-shaped CTA (different angle from first email)

Prompt Patterns

Pattern 1: Pain-Theme Segmentation

Map 3-5 pain themes from your hypothesis set, then branch P1 based on which theme fits each recipient.When to use: You have a hypothesis set with distinct pain points and enrichment data to match companies to themes.

Pattern 2: Role-Based Emphasis

Vary the angle based on the recipient’s seniority and function:

Analysts/researchers: precision, coverage, manual work reduction
Directors/VPs: speed, cost discipline, cross-team visibility
C-suite: strategic advantage, competitive edge, scale

When to use: Your list spans multiple seniority levels within the same vertical.

Pattern 3: Post-Event Outreach

Use a shared event as the opener.Structure:

P1: Event reference + observation about a trend discussed
P2: Simple question about how they currently handle [relevant process]
P3: Product explanation with concrete outcome
P4: Soft CTA (no hard meeting ask)

When to use: After a conference, webinar, or industry event.

Pattern 4: Multi-Email Sequence

First email + follow-up with different angles.Structure:

Email 1: Hypothesis-driven opener → product value → CTA → proof point
Email 2 (follow-up): Different case study → different capability angle → sector-shaped CTA

Rules:

Follow-up must use a different value angle than email 1
Never say “quick follow-up” or “circling back”
Follow-up is shorter (≤60 words vs ≤120)

Cross-Campaign Defaults

Rule	Default
Max words (first email)	Varies by structural variant (60-120)
Max words (follow-up)	60
Paragraphs (first email)	Varies by structural variant (2-4)
Paragraphs (follow-up)	2
Greeting format	”Hey ,“
Firm mentions	At most once
Sector naming	Always explicit, never “sectors like yours”
Output format	JSON (keys: recipient_name, recipient_company, subject, greeting, paragraphs per variant)
Prompt location	`claude-code-gtm/prompts/{vertical-slug}/`

Next Steps

After building the prompt template:

Proceed to email-generation to run the prompt against your contact CSV
Or review the prompt and refine voice rules
Or test with a small sample before full generation

Get Started

Core Concepts

Skills Reference

Guides

API Integration

Email Prompt Building

Overview

When to Use

Architectural Principle

What This Skill Reads

What This Skill Produces

Building a Campaign Prompt

Structural Variants

Variant Details

Follow-up Email

Prompt Patterns

Cross-Campaign Defaults

Next Steps

Build docs developers (and LLMs) love

Get Started

Core Concepts

Skills Reference

Guides

API Integration

​Overview

​When to Use

​Architectural Principle

​What This Skill Reads

​What This Skill Produces

​Building a Campaign Prompt

​Structural Variants

​Variant Details

​Follow-up Email

​Prompt Patterns

​Cross-Campaign Defaults

​Next Steps

Build docs developers (and LLMs) love

Overview

When to Use

Architectural Principle

What This Skill Reads

What This Skill Produces

Building a Campaign Prompt

Structural Variants

Variant Details

Follow-up Email

Prompt Patterns

Cross-Campaign Defaults

Next Steps