zvadaadam/az-skills/product-rehearsal

Product Rehearsal

Pretend the feature shipped yesterday and follow the people or agents who meet it. Walk far enough into their experience to discover what the proposal overlooks, revise it, then rehearse the revision. The deliverable is a better decision about what to build, including whether to build it at all.

This is the behavior-level sibling of implementation-rehearsal: decide here what the product should do, then rehearse there how the code would change. If only this skill is installed, hand the settled behavior to ordinary planning.

Scope and evidence

• Identify the proposed feature, who it is for and what is already decided from the conversation. If the user has committed to building it, rehearse to find the corner cases and shape the behavior; do not relitigate the decision unless a finding makes the feature harmful.
• Read the product as it exists: the screens, commands, docs, help text, tool or skill descriptions, defaults, error messages and whatever feedback, issues or usage notes are available. The actor in the rehearsal arrives through these, so the walk is only as real as your reading of them. Ask only for missing information that would materially change the recommendation; continue independent investigation meanwhile.
• Keep the product unchanged during the rehearsal: no source, copy, configuration or doc edits. Analysis artifacts and separately requested deliverables are allowed within their stated scope. Observing how the current product behaves, such as running an existing command to see its output, is fine when its side effects are authorized.
• Distinguish observed facts about the current product or its real users, with a source; predicted reactions with a concrete trigger; and unknowns that only real users, usage data or a trial can settle. A predicted reaction is not user research. Do not invent quotes, personas with biographies, adoption numbers or satisfaction scores; they make speculation look like evidence.
• When there is no existing product or user evidence to read, say so. Shrink the rehearsal to the walk and a short list of unknowns instead of filling the gap with imagined users.

Pass 1 — Walk the shipped feature

Choose one representative actor in one real situation: who they are in terms of what they already know and already do with the product, and what they are trying to get done when they meet the feature. An actor may be a person, or an agent that consumes a tool, command, API or skill. When the feature touches several kinds of actor, walk the one it is mainly for; the others enter in Pass 2 as the people it happens to.

Follow them in order:

1. Discovery — how they learn the feature exists, through a surface that exists today or one the proposal adds. A feature nobody finds has no corner cases.
2. First use — exactly what they type, click or call, and what they need to know beforehand.
3. Result — what they see or receive, and how they tell it worked.
4. Next step — what they do with the result, and what the tenth use looks like compared with the first.

Write the concrete artifacts the actor would meet instead of describing them: the one-line changelog entry, the command and its help text, the tool or skill description, the empty state, the main error message. Drafting them is the cheapest test of the proposal; when the changelog line cannot be written plainly, the feature is not yet defined, and that is the first finding.

If a necessary detail is missing from the proposal, mark it as an open product decision instead of silently choosing the convenient behavior. To keep drafting, assume the likeliest answer and carry the assumption into the open decisions. If the walk exposes a problem the product already has, flag it and say whether fixing it is a prerequisite or separate work; do not fold it into the feature unannounced.

Stay at the level of what the actor experiences. A technical constraint belongs here only when it changes that, for example by ruling out a behavior or forcing a wait.

Pass 2 — Try to break the experience

Pick a few situations grounded in the product you read. Prioritize those that could change the decision; do not fill a generic checklist or manufacture a fixed number of findings.

For each material concern, connect:

Actor and situation → what they do → what they experience → why it is bad → evidence → smallest product change or discriminating check.

Useful questions when applicable:

• Who did not ask for this? Would an existing habit, script, shortcut or saved workflow now behave differently, and would they be told?
• What happens in the states the happy path skips: nothing yet, far too much, stale, half-finished, shared with someone else, offline, without permission?
• What does the actor believe the feature does, given its name and placement, and where does that belief diverge from what it does?
• Can they undo it, stop it midway or turn it off? What is left behind?
• What does it cost the people who never use it: another option, another prompt, a slower default, a more crowded screen?
• Does it overlap an existing feature so that there are now two ways to do one thing, and which one should they pick?

When the actor is an agent, the same pass applies with different failure shapes:

• Would the description make an agent reach for it at the right moment, miss it, or fire on requests that belong to a neighboring tool or skill?
• What does it cost in context and steps when it fires and turns out not to be needed?
• Does it still work installed alone, in a non-interactive session, without the tools or siblings it assumes?
• Where the instructions are ambiguous, what will an agent with no access to your intent do? Agents follow the text literally and do not ask.
• What does the human on the other side see: a better result, or just more output and a slower turn?

Do not change the recommendation merely to demonstrate critique. Keep a sound proposal when the counterexamples do not apply, and explain why with evidence.

Pass 3 — Rehearse the revision

Change the smallest part of the behavior that addresses the substantiated concern: a default, a name, a scope cut, a confirmation, an entry point, or a missing piece the feature does not hold together without. Walk the representative actor and the strongest counterexample through the revised feature. Check that the correction does not add a setting nobody will find, a second mode to explain, or a new surprise for a different actor.

Usually two or three passes are enough; stop when another pass yields no material change to the proposal, or the remaining uncertainty needs real users, real usage or a product decision only the user can make. Name that uncertainty instead of continuing speculative rounds.

For an agent-facing feature, one uncertainty can sometimes be settled cheaply: give a fresh agent only the drafted description or instructions plus a realistic request, and observe what it does. Treat this as an optional discriminating check when a specific doubt justifies its cost and the user's setup allows spawning agents. One run is an anecdote; report it as such.

Narrowing is a normal outcome. If the feature only works for the representative actor and hurts the others, recommend the narrower version, a later one, or not building it.

Deliver the decision

Lead with the revised recommendation: build as proposed, build a revised version, or do not build. Summarize the findings and revisions, not internal deliberation or a pass-by-pass account:

• What changed in the proposal: the few important corrections, with evidence and their predicted consequences. If nothing changed, say what survived scrutiny.
• How it is used: the final walk in a few lines, with the drafted artifacts that matter, such as the command, description or key message.
• Where it will still be bad: the trade-offs accepted knowingly and who bears them. Leave out corner cases the revision already resolved.
• What proves it: the signals to watch after shipping and the questions to put to real users, plus unresolved product decisions and who can make them. Separate what was observed from what is predicted.
• What to leave out: only tempting additions that were actually considered and rejected, with the reason.

Size the answer to the feature. The reader wants the decision and the few findings behind it, so a small feature deserves a short answer: give each finding its trigger, consequence and fix in a few lines, and drop any heading that has nothing material under it. Mark what is observed or predicted inline where it matters instead of opening with a disclaimer.

Do not present the rehearsal as validation; no user has seen the feature. When the request is rehearsal only, finish with the recommendation. When the user already authorized rehearsal followed by building, complete this phase and continue within that authorization; the skill adds no approval gate.

AZ Skill Feedback

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