Free Personalized Email Research Prompt

Short answer: Find one relevant reason to reach out and reject weak personalization before writing.

Built by StackSwap · Updated August 28, 2026

OUTPUTResearch brief, signal confidence, opener options, and no-send rule.
REPLACESFabricated personalization.
RUNS INChatGPT · Claude · Codex

BEFORE YOU COPY

Bring the context. Skip the blank page.

Collect the target account or segment, trigger or hypothesis, source evidence, channel constraints, sender context, qualification threshold, compliance limits, and reply or measurement plan. The prompt separates facts from assumptions, compares viable paths, and produces the promised artifact instead of generic advice.

  1. Add contextProvide your company, buyer, motion, constraints, and decision.
  2. Run the workflowPaste the free prompt into ChatGPT, Claude, or Codex.
  3. Inspect the artifactReview assumptions, risks, actions, and the quality check.

FULL PROMPT · FREE FOREVER

Copy the full GTM workflow.

No account or email required. Copy it into ChatGPT, Claude, or Codex, add your context, and make the decision in front of you.

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## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: personalized-email-research
description: "Research an account enough to write relevant outbound without fabricating personalization, over-collecting data, or confusing activity with intent."
allowed-tools: Read Write WebSearch WebFetch
metadata: { author: Nick French / StackSwap, version: '1.0', product: Operator Playbook }
---
# Personalized Email Research

Personalization is useful when it changes the relevance of the hypothesis, not when it adds trivia.

## When to use it
Use before writing a small, targeted email set for a defined account and motion. Do not use it for mass scraping or invented compliments.

## Inputs to collect
Collect account, role, ICP, trigger window, offer, allowed sources, privacy/compliance boundaries, evidence standard, sender context, and no-send rules.

## Method
1. Identify the relevant job, trigger, business context, and likely alternative.
2. Gather only evidence that supports or challenges the hypothesis; record source and date.
3. Separate fact, inference, and unknown; reject weak or creepy details.
4. Draft a concise opener tied to the trigger, relevance thesis, proof, and low-friction next step.
5. Create reply branches and a review checklist before sending.

## Output
Produce a research ledger, trigger and hypothesis, source/date notes, personalization candidates, send/no-send decision, email draft, reply branches, and QA checklist.

## Verification and failure modes
Verify every personalized claim and review for relevance and dignity. Guard against fabricated details, stale signals, surveillance tone, generic flattery, and CTA pressure.

## Quality gate
The recipient should understand why the message is relevant without the sender claiming knowledge they do not have.

Free forever. No email gate.

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QUESTIONS

About this free prompt

What does this personalized email research prompt help with?

Find one relevant reason to reach out and reject weak personalization before writing.

Who should use this personalized email research prompt?

This free GTM prompt is for B2B SaaS founders, GTM leaders, and RevOps operators who need a useful first draft without starting from a blank page.

What should I add before running this personalized email research prompt?

Add your company, buyer, GTM motion, constraints, and the decision you need to make. Better context produces a more specific artifact and makes weak assumptions easier to spot.

What output does this personalized email research prompt produce?

Research brief, signal confidence, opener options, and no-send rule. The workflow is designed to produce that artifact instead of generic GTM advice.

Can I use this personalized email research prompt in ChatGPT, Claude, or Codex?

Yes. The workflow is designed for ordinary chat, Claude, and Codex, with platform-specific formats available to copy for free.

How do I get a better result from this personalized email research prompt?

Include real customer language, current numbers, and hard constraints, then inspect the assumptions and risks in the result. Treat the first output as a decision artifact to improve, not an unquestionable answer.

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