Free PQL Motion Design Prompt

Short answer: Decide whether product usage can create a responsible sales signal and design the handoff.

Built by StackSwap · Updated August 28, 2026

OUTPUTSignal definition, thresholds, routing, playbook, and measurement.
REPLACESUsage-score vanity metrics.
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

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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: product-qualified-lead-motion
description: "Design a PQL motion from meaningful product behavior, fit, intent, routing, intervention, and conversion evidence."
allowed-tools: Read Write WebSearch WebFetch
metadata: { author: Nick French / StackSwap, version: '1.0', product: Operator Playbook }
---
# Product-Qualified Lead Motion

A PQL is not an activity threshold. It is product evidence that, combined with fit and context, justifies a useful next action.

## When to use it
Use when product usage can reveal value, intent, or expansion and the team needs a sales or lifecycle motion. Do not use it to label every active user as sales-ready.

## Inputs to collect
Collect ICP, activation/value events, usage quality, account/user identity, intent, timing, historical conversion, sales capacity, routing, messaging, privacy, and success criteria.

## Method
1. Define the customer outcome and behavior that demonstrates progress toward it.
2. Separate fit, activation, intent, frequency, depth, and account context.
3. Set threshold, freshness, disqualifier, routing, SLA, and intervention rules.
4. Design handoff and feedback between product, marketing, sales, and CS.
5. Backtest and monitor false positives, false negatives, conversion, and customer experience.

## Output
Produce PQL definition, signal dictionary, scoring/routing rules, handoff SLA, intervention play, dashboard, test set, validation report, and iteration plan.

## Verification and failure modes
Backtest against qualified progression and customer outcomes. Guard against usage vanity, self-serve users being pressured, stale signals, duplicate ownership, and sales activity without value.

## Quality gate
The threshold must represent meaningful customer progress and trigger a relevant, consent-respecting action.

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QUESTIONS

About this free prompt

What does this pql motion design prompt help with?

Decide whether product usage can create a responsible sales signal and design the handoff.

Who should use this pql motion design 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 pql motion design 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 pql motion design prompt produce?

Signal definition, thresholds, routing, playbook, and measurement. The workflow is designed to produce that artifact instead of generic GTM advice.

Can I use this pql motion design 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 pql motion design 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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