Free Cold Call Opener Prompt
Short answer: Create a concise, permission-based opener rooted in a real problem and plausible relevance.
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.
- Add contextProvide your company, buyer, motion, constraints, and decision.
- Run the workflowPaste the free prompt into ChatGPT, Claude, or Codex.
- Inspect the artifactReview assumptions, risks, actions, and the quality check.
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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: cold-call-opener
description: "Create and test a respectful cold-call opener anchored in a relevant hypothesis, permission, and a clear next step."
allowed-tools: Read Write WebSearch WebFetch
metadata: { author: Nick French / StackSwap, version: '1.0', product: Operator Playbook }
---
# Cold Call Opener
The opener earns a conversation; it does not try to complete the sale in the first ten seconds.
## When to use it
Use for a defined segment, trigger, persona, and offer. Do not use one opener across unrelated buyers or as a trick to hide the call's purpose.
## Inputs to collect
Collect persona, problem hypothesis, trigger, relevance evidence, alternatives, sender context, permission/compliance limits, desired next step, and objection branches.
## Method
1. State who you are and why you called in plain language.
2. Connect one relevant trigger to one problem hypothesis and ask permission to test it.
3. Prepare a concise value/context line, a question, and a respectful exit.
4. Define responses for not interested, bad timing, send information, incumbent, and wrong person.
5. Track conversation quality, qualification, and next-step progression, not dials alone.
## Output
Produce opener variants, trigger/evidence notes, first question, objection branches, voicemail option, qualification cues, practice rubric, and measurement plan.
## Verification and failure modes
Roleplay with the target persona and inspect whether the opener earns a relevant response. Guard against manipulation, fake familiarity, monologues, feature dumping, and volume without learning.
## Quality gate
The opener is ready when it is truthful, specific enough to be relevant, easy to understand, and safe to decline.Free forever. No email gate.
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QUESTIONS
About this free prompt
What does this cold call opener prompt help with?
Create a concise, permission-based opener rooted in a real problem and plausible relevance.
Who should use this cold call opener 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 cold call opener 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 cold call opener prompt produce?
Openers, branches, objection paths, and practice notes. The workflow is designed to produce that artifact instead of generic GTM advice.
Can I use this cold call opener 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 cold call opener 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.