Product discovery

SkillDev tools

This skill helps your AI guide product discovery work, so ideas are checked out before you commit time and resources to building them. Once added, your AI can map the assumptions behind a product opportunity, plan discovery sprints, and test problem-solution fit.

Available today. Use it from your connected AI after setup.

After adding it, share a product idea or opportunity with your AI and ask it to map the assumptions or plan a discovery sprint around it.

Then ask your AI: use the Product discovery skill

What your AI can do with it

  • Validate product opportunities before committing delivery resources
  • Map the assumptions behind a product idea
  • Plan discovery sprints
  • Test problem-solution fit before building

What this skill tells your AI

The instructions your AI receives, as published by cbrock84/headcount in plugins/product/skills/product-discovery/SKILL.md and read by ahel’s review.

Discovery is how you find out you were wrong while it is still cheap. A process that never kills anything is not discovery, it is a preparation ritual with research attached.

Start from the assumption that would sink this if it were false

Every idea rests on a stack: that the problem exists, that people care enough to change what they do, that your approach solves it, that they would pay, that you can build and deliver it. They are not equally uncertain, and testing them in order of comfort is how teams spend six weeks confirming the safe one.

Write the assumptions down, mark the one that would be most damaging to be wrong about, and test that one first. Usually it is the second: the problem is real, and people are living with it comfortably enough not to move.

Recruit the people who have the problem, not the people who are easy to reach

Interviewing your friendliest customers produces reliable encouragement. Talk to people who churned, people who evaluated and chose something else, and people who solved it another way — those three groups carry most of the information.

Five to eight conversations in a segment usually exhausts the new material. If you are still hearing new things at eight, the segment is too broad.

Interview about the past, not about the future

People are poor at predicting their own behavior and generous when asked to react to an idea. They are reliable narrators of what they actually did.

  • Ask about the last time it happened. What triggered it, what they tried, what it cost them, what they did instead.
  • Follow the workaround. A spreadsheet someone maintains by hand every week is stronger evidence of a real problem than any amount of enthusiasm about a proposed feature.
  • Do not describe your solution until the end, and treat everything said after that point as weaker evidence.
  • Silence is a tool. Most of the useful material arrives after the pause you were tempted to fill.

"Would you use this?" and "would you pay for this?" produce answers that do not predict anything. What predicts is whether they have already spent money or time on the problem.

Weigh what people do over what they say

Rank evidence honestly: what they have paid for, what they have built themselves, what they do in an unprompted usage log, what they say in an interview, what they say in a survey. A prototype someone tries in front of you sits high on that list; an enthusiastic reaction to a mockup sits low.

Test with the cheapest thing that could produce a real signal

Match the artifact to the assumption. Demand risk is testable with a landing page or a sales conversation, usability risk with a rough prototype, feasibility with a spike. Building a working version to test whether anyone wants it is the expensive way to answer the cheap question.

Define the outcome that would change your mind before you run it. A test with no failing threshold is a demonstration.

End with a decision

Discovery output is build, do not build, or a specific next test — not a deck of themes. Write what you learned, what you still do not know, and what you are doing about it, and keep the record of the ideas you killed and why. That record is what stops the same idea returning every six months.

Never

  • Test the assumption you are most confident about first.
  • Ask whether someone would use a thing instead of what they did last time it came up.
  • Recruit only from customers who already like you.
  • Run a test with no threshold that would have counted as failure.

Signals

GitHub stars
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Forks
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
product-discovery
Source
github.com/cbrock84/headcount