Tasteroll logo

Tasteroll

Organization
KyaniteLabs
tasteroll

Rapid prototyping through constrained randomness. Audits what exists, fixes what is broken, generates fresh design candidates from context, then rolls the dice between valid alternatives. Lock what works, re-roll what doesn't, converge on a direction. Use for design exploration, rapid prototyping, or when a user wants surprise within guardrails.

Overview

PublisherKyaniteLabs
Repositorytastecheck
Skill nametasteroll
Stars
10
Forks
7
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by KyaniteLabs on GitHub. Read the source before you install it.

Installation

Install the Tasteroll AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/KyaniteLabs/tastecheck/tree/main/skills/tasteroll
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/KyaniteLabs/tastecheck.git /tmp/tastecheck
mkdir -p .claude/skills
cp -r /tmp/tastecheck/skills/tasteroll .claude/skills/tasteroll
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tasteroll in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tasteroll on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tasteroll is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Tasteroll (roll for taste, keep what sticks)

Tasteroll audits what exists, asks at most three questions, fixes what is broken, then rolls valid alternatives. Lock what works; re-roll what does not. Project constraints produce taste even when choices are random.

The pipeline

1. AUDIT     → scan what exists → mandatory findings (craft-only)
2. INTAKE    → infer from context/repo OR 3-question mini-interview
3. FIX       → resolve all findings (seed-independent, non-negotiable)
4. GENERATE  → fresh candidates for open dimensions (context-aware)
5. ROLL      → seeded pick between valid alternatives
6. LOCK      → keep what works, re-roll what doesn't, converge
7. GATE      → relaxed gate (readable + WCAG AA + self-contained + inert)

1. Audit (mandatory fixes)

Scan rendered surfaces, source CSS, repo files, and content. Resolve every non-negotiable craft finding before rolling:

  • Contrast: body < 4.5:1 or UI < 3:1 on actual surfaces
  • Spacing: no scale, arbitrary margins with no token system
  • States: interactive elements missing hover/focus/active/disabled
  • Tells: named AI slop patterns present (gradient, centered hero, 3 cards)
  • Accessibility: missing alt, unlabeled controls, broken heading order
  • Structure: missing main landmark, no skip link, no focus-visible

Flag findings outside craft scope (bad copy, content gaps, broken features) and hand them off; tasteroll does not fix them.

A single-word "preserve" overrides an intentional low-contrast or missing-state choice; otherwise resolve every finding.

For greenfield, scan brief/content for contradictions, missing scope, and vague direction.

2. Intake

Choose by available signal:

Infer (default): read the conversation and scan repo files (README, CSS, package.json, content). Skip questions when product and audience are clear.

Mini-interview (fallback): ask three questions when inference is thin:

  1. What are you building? → unlocks structure, density, IA
  2. Who is it for? → unlocks personality, accessibility posture, complexity
  3. One word for how it should feel? → unlocks aesthetic, accent direction

Three answers cover the brief without the full 11-field design-system-interview.

3. Fix (deterministic)

Resolve every finding here. Separate seed-independent "resolved findings" from random "rolled choices" in the output.

4. Generate

For each open dimension (personality, aesthetic, type, color_mode, density, rhythm, signature, imagery, motion, accent), generate 2–5 candidate options from audit/intake context at roll time; no static list.

Candidates must satisfy compatibility rules in assets/design-rails.json: compact density/metronomic rhythm, airy/syncopated, brutalist/restrained motion, etc. No conflicts.

Respect locked dimensions from previous rolls.

5. Roll (seeded pick)

The PRNG (assets/tasteroll-engine.js) picks one candidate per open dimension. Same seed

  • candidates → same roll, always; seeds make rolls reproducible/shareable.

If Chance (kyanitelabs/chance) is available via MCP, prefer it for multi-source entropy mixing and reproducible audit trails; the inline engine is the fallback.

6. Lock and re-roll

Mode:

  • One-shot: roll once; take it or re-roll from scratch.
  • Iterative: roll → review → lock working dimensions → re-roll unlocked only → converge. Each re-roll increments the seed and excludes shown directions.
  • Shotgun: roll N seeds → compare side by side → pick one to develop.

Locked dimensions and resolved findings persist; only open dimensions vary.

7. Gate

Tasteroll output is a personal artifact/prototype, not shipped; use a lighter gate than tastecheck-pass:

  • Readable: start with body ≥ 1rem, measure 58–75ch, and line-height ≥ 1.5; a brief may justify another measure with rendered evidence
  • WCAG AA: body ≥ 4.5:1, UI ≥ 3:1 (verified on the rolled surfaces)
  • Self-contained: no external requests in exports
  • Inert: no scripts in exported HTML
  • Keyboard-reachable: all interactive elements focusable with visible focus

Escalate shipped public surfaces to tastecheck-pass.

Invariants

Invariant floors and brief defaults live in assets/design-rails.json. Contrast, keyboard reachability, self-containment, and truthful findings stay invariant. Briefs may override measure, line-height, weight, density, texture, radius, accent count, and section rhythm only with reasons and rendered proof.

Rails contain no option lists; generate options fresh.

Difference

  • v0 / LLM generators: no seed, audit, rules, reproducibility
  • Coolors: random colors, no system, structure, gate
  • Tasteroll: audit fixes + context-aware candidates + seeded pick + lock/reroll + complete design system + quality gate

References

  • assets/design-rails.json — dimensions, floors, defaults, scope, questions, rules.
  • assets/tasteroll-engine.js — pasteable xoshiro128++ PRNG with lock, reroll, shotgun, and exclude operations.

How to deliver

Deliver seed, deterministic findings, seed-dependent choices, DESIGN-SYSTEM.md, gate evidence, and design-system-interview handoff fields.

"Roll again" increments the seed; never silently reuse it with different answers.

Evidence

StatusReasonRemediationEvidenceProvenance
audit — findings and their resolution status
intake — inference result or mini-interview answers
fixes — resolved findings (seed-independent)
roll — seed, locked dimensions, rolled choices
gate — relaxed-gate contrast, readability, self-containment

Contract (generated)

Canonical detail: contract.json.

  • Route: Rapid design exploration or surprise direction without a full interview (+2 in contract.json); avoid: The user wants to commit to a specific direction by hand (+2 in contract.json)
  • Exclude: Do not roll outside the design rails or bypass the audit findings (+3 in contract.json)
  • Stop / handoff: Stop when the output will be a shipped public surface — escalate to tastecheck-pass strict gate (+1 in contract.json); receives [design-system-interview, improve-existing-website] -> sends [color-system, web-typography, spacing-system, theming, responsive-layout, art-direction, micro-motion, component-states, data-viz, empty-states, form-ux, humanize-copy]
  • Output: A seeded DESIGN-SYSTEM.md with resolved findings, rolled choices, and gate evidence
  • Evidence: table_with_evidence with status, reason, remediation, evidence, provenance.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tasteroll AI skill do?

Rapid prototyping through constrained randomness. Audits what exists, fixes what is broken, generates fresh design candidates from context, then rolls the dice between valid alternatives. Lock what works, re-roll what doesn't, converge on a direction. Use for design exploration, rapid prototyping, or when a user wants surprise within guardrails.

Why use Tasteroll on TypingMind?

Because you install it once and use it with any model. Tasteroll is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tasteroll in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/KyaniteLabs/tastecheck/tree/main/skills/tasteroll. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tasteroll?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tasteroll?

As many as you like. As long as a model supports skills, you can use Tasteroll with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tasteroll AI skill free?

Yes. It is published on GitHub by KyaniteLabs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇