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.
Adopted by
Most recently taken up by
- gaoyu06/harnesstheir own version, first seen Nov 8, 2026
- ishandutta2007/Awesome-Claude-Skillstheir own version, first seen Nov 8, 2026
- domengabrovsek/agent-configtheir own version, first seen Nov 8, 2026
- domengabrovsek/claudetheir own version, first seen Nov 8, 2026
- The-Vibe-Company/quivrtheir own version, first seen Nov 8, 2026
- miklschmidt/archboardtheir own version, first seen Nov 8, 2026
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.
CoralGameSkillplaytest-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
lilangMaxSkillsoak-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
lilangMaxSkillteam-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
CoralGameSkillteam-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
Other versions194 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 194
- mattpocock/skills★ 309ksame name
- emilkowalski/skill★ 55ksame name
- emilkowalski/skills★ 55ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- Donchitos/Claude-Code-Game-Studios★ 27ksame name
- GreyDGL/PentestGPT★ 16ksame name
- spree/spree★ 16ksame name
- usekaneo/kaneo★ 9.7ksame name
- podman-desktop/podman-desktop★ 8.1ksame name
184 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository39 other files it gives its agents.
- AGENTS.mdAGENTS.md
- authoringSkill
- chrome-devtoolsSkill
- gamificationSkill
- imagegenSkill
- improve-architectureSkill
- instructionsSkill
- monorepoSkill
- reverse-engineerSkill
- runbookSkill
- security-reviewSkill
- setup-harnessSkill
- skillsSkill
- unslopSkill
- video-reportSkill
- youtube-transcriptSkill
- code-reviewSkill
- debugSkill
- interviewSkill
- researchSkill
- workSkill
- adsenseSkill
- astryxSkill
- cloudflare-cliSkill
- dokploy-best-practicesSkill
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- tanstack-storeSkill
- tauriSkill
- vite-plusSkill
- web-assets-optimizationSkill
- web-extensionSkill
- zodSkill
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
- Distinctive: line 35
Run every scenario yourself before showing it.
- Distinctive: line 50
Keep the prototype branch after the decision.
- Very specific: line 55
When /work sent you here, return the result to it.
“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.
Files
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 about | Build and observe |
| Appearance, organization, interaction | Variants 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 transitions | A small harness with a reset, a normal scenario, a scenario that could break the model (ordering, repetition, boundary), and a rejected operation |
| Integration, capacity, performance | A 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.
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
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.

