agentleFS
mehrad-dmmehrad-dm/mastermind/skills/prototype/SKILL.md

prototype

Skill23 starsChanged 2 months agoMITView on GitHub

Use when the right approach is genuinely unknown and reality will teach faster than planning: a risky unvalidated assumption, an unfamiliar integration, \"will this even work\", \"try something quick\". The result is explicitly throwaway and does not ship.

194 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

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

    playtest-report

    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

2 rules. 0 are common, 2 are distinctive, and 0 are 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.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Extending the box is a decision, not drift: Only extend when the attempts narrowed the question rather than wandering: say so explicitly, …

    line 32
  2. Distinctive:

    Keep it isolated (a scratch branch/dir) so it can't leak into production.

    line 35

“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

MasterMind: Prototype

Tight feedback loops beat big plans when the path is unclear (~/.mastermind/engineering/core/agent-loop.md, mindset.md → design it twice). A prototype buys knowledge cheaply: the discipline is keeping it knowledge, not product.

Method

  1. Name the question: the ONE risky unknown this prototype will answer ("can the Worker stream a >20 MB file?", "does this library handle X?"). One named question is the entry price, find it first.
  2. Build the smallest thing that answers it: hard-code, skip error handling, skip tests, skip polish. Speed over quality; it's throwaway.
  3. Extract the learning: what did reality teach? Write down the answer and any surprises.
  4. Throw it away and rebuild to standard: the real version gets the full loop (design, rigor, tests, review). Ship the rebuild; the prototype gets discarded.

Rules

Time-box: 5 build-and-run attempts, roughly 30 minutes: one question needs a handful of tries, not a day; proportionality over ceremony. An attempt is one edit → run → observe cycle against the named question; count them out loud as you go, so the box is observable and not a vibe. Attempt 5 finished without an answer = the box is spent, whatever the clock says.

When it expires, stop. That's a result, not a failure. Report: what you learned, what is still unknown, and one recommendation: proceed (answer is yes, build it properly) / different approach (this path is wrong, here's the next one) / needs more investigation (name what a second prototype would target). Then discard the code: it stays throwaway and does not ship, same as a prototype that succeeded.

Extending the box is a decision, not drift. Only extend when the attempts narrowed the question rather than wandering: say so explicitly, state the new bound (e.g. "3 more attempts"), and stop there.

Keep it isolated (a scratch branch/dir) so it can't leak into production. The deliverable is the learning, not the code.

Output

The answer to the question + the surprises, and a clear "now build it properly" recommendation (or a "don't build it, here's why"). When the prototype served a feature in specs/, record the question and the answer in that feature's research.md, so blueprint builds on it instead of re-asking it.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.