Reciprocal relationship: the community prompt pack

This pack helps you test whether high comment volume automatically creates a strong community. It turns the idea into a small decision you can inspect this week, without inventing customer evidence or hiding behind more output.

Start with the evidence

I need to decide whether high comment volume automatically creates a strong community.
Review the material I paste below.
Separate observed behaviour, direct statements, opinions, and assumptions.
Quote the evidence behind every conclusion.
Do not suggest solutions yet.
End with the three most important missing facts.

Useful signals to look for:

Comments contain only keywords
People never return after delivery
Members respond to one another

Run the smallest honest test

The test is to follow a keyword interaction with a real question and observe who returns or helps another person. Decide what passing and failing mean before you see the result. Otherwise almost any outcome can be explained as good news after the fact.

Design a seven-day test to follow a keyword interaction with a real question and observe who returns or helps another person.
The risky assumption is: high comment volume automatically creates a strong community.
Use real behaviour, not hypothetical intention.
Give me the participant, task, evidence to capture, pass condition,
failure condition, and the decision each result should trigger.
Keep the test small. Do not propose a new product or campaign.

Ask for a real timeline

Write five interview questions about a specific past event.
Cover what happened, what they tried, the workaround, the cost,
and what happened afterward.
Do not ask what feature they want or whether they would use an idea.
Add one short follow-up beneath each question.

Read the result without rescuing it

Review these notes as a skeptical product partner.
Create two columns: what happened and my interpretation.
Flag any interpretation unsupported by an observation.
Give the strongest case for continuing and the strongest case for stopping.
Name the one missing fact most likely to change the decision.

Make the decision

Use automation for delivery, then invest human attention where an ongoing relationship appears.

Use this short decision note:

Decision: What happens now?
Evidence: What observed behaviour supports it?
Risk: What could still make it wrong?
Next check: What will you inspect and when?
Not doing: What attractive option is waiting?

One prompt to run on everything

Help me decide whether high comment volume automatically creates a strong community.
1. Separate behaviour, statements, opinions, and assumptions.
2. Quote the evidence behind each important claim.
3. Name the weakest assumption carrying the most risk.
4. Design a seven-day test to follow a keyword interaction with a real question and observe who returns or helps another person.
5. Set pass and failure conditions before the test.
6. Give five past-behaviour interview questions.
7. End with a decision note and one thing we are deliberately not doing.
Do not invent customer facts. Mark missing evidence clearly.

Download the runnable pack

This is a complete starter for the workflow in this guide, not a screenshot. The safe default validates fictional input and returns a deterministic, reviewable draft. It does not publish content, message a customer, change pricing, edit production data, or execute another external action.

Run the downloaded folder locally:

npm test
npm run validate
npm run sample
cp .env.example .env
docker compose up --build

The ZIP includes an importable n8n webhook, a dependency-free Node 22 service, Docker Compose, GitHub Actions validation and manual-run workflows, fictional sample input, expected dry-run output, and tests. If you later enable Claude, keep the API key in the service environment. The n8n export contains no credentials. Live mode still returns a draft for approval.