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FirzusFirzus/agent-skills/skills/stack/prototype/SKILL.md

prototype

Skill4 starsChanged 19 days agoMITView on GitHub

Answer a design or feasibility question with the smallest observable experiment, such as comparing interface variants, exercising domain rules, or measuring a technical limit. Use when a decision needs observed behavior rather than more discussion or source research; not for building the settled feature.

194 other projects use a version of this.

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Projects with it also use playtest-report, soak-test and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • CoralGameSkill

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    Generates a structured playtest report template or analyzes existing playtest notes into a structured format. Use this to standardize playtest feedback collection and analysis.

    Also in 12 projects that use this

  • lilangMaxSkill

    soak-test

    Generate a soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases.

    Also in 9 projects that use this

  • lilangMaxSkill

    team-live-ops

    Orchestrate the live-ops team for post-launch content planning: coordinates live-ops-designer, economy-designer, analytics-engineer, community-manager, writer, and narrative-director to design and plan a season, event, or live content update.

    Also in 9 projects that use this

  • CoralGameSkill

    team-polish

    Orchestrate the polish team: coordinates performance-analyst, technical-artist, sound-designer, and qa-tester to optimize, polish, and harden a feature or area for release quality.

    Also in 14 projects that use this

  • +5 more from lilangMax
  • +3 more from CoralGame

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 179 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run every scenario yourself before showing it.

    line 35
  2. Distinctive:

    Keep the prototype branch after the decision.

    line 50
  3. Very specific:

    When /work sent you here, return the result to it.

    line 55

“Common” is measured: kept by at least 6 in 10 of the 179 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Prototype a decision

Build the smallest experiment that answers one question in a representative environment. A prototype produces evidence for a decision, not a production feature, and its approval says nothing about production reliability.

1. Bound the question

State the question, the decision it informs, the scenarios to observe, and the evidence that will end the experiment. Separate what can be measured, which you settle yourself, from what needs the user's judgment, such as which variant feels clearer. If source evidence alone would answer it, research instead; if the behavior is already settled, show the evidence and stop.

2. Build it where it counts

Reuse the real product, components, and data shape as far as the question needs: isolated logic cannot show real rendering, device interaction, or performance. Work on a dedicated branch, prototype/<subject>, without disturbing the user's checkout or uncommitted work. Run it on a copy of the data or on fixtures, with external services simulated, and label every simulated dependency; an experiment that needs a real external effect asks the user first.

Question aboutBuild and observe
Appearance, organization, interactionVariants that differ in structure or primary interaction, in the real surroundings with representative content, compared under the same data; include empty, error, and narrow-screen states
Rules, data, state transitionsA small harness with a reset, a normal scenario, a scenario that could break the model (ordering, repetition, boundary), and a rejected operation
Integration, capacity, performanceA measurable claim with a threshold set before measuring, a baseline, one changed variable, and repeated runs with their spread

Run every scenario yourself before showing it.

3. Show and record

Give the user the runnable experiment, with how to start and reset it, and keep it available during their review. Present the observations and trade-offs, then ask only for the judgments the experiment defines. A negative result is a valid answer.

With the user's agreement to the experiment, commit the reviewed version and push its branch, so the recorded commit can be reached from any session; the agreement covers this push, never a pull request. Without a remote, or when the push fails, the branch stays local and the record says so. Record the question, the exact commit, how to run it, what was observed, the user's judgment, and what remains untested. When /work runs it as a Linear issue, return this record and /work posts it on the issue; otherwise keep it in the conversation. Keep the prototype branch after the decision; the implementation reuses parts of it deliberately, never by merging it whole.

Done: the question is answered by observed evidence and the judgments it needed are given. Waiting: the user's judgment is open; keep the experiment running. When /work sent you here, return the result to it.

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