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taste-engine

Turn subjective quality intent into executable hard gates, weighted rubrics, exemplars, anti-exemplars, blind comparison, and independent judge evidence.

Skill by frankxai

IndexedMIT★ 11
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About

Turn subjective quality intent into executable hard gates, weighted rubrics, exemplars, anti-exemplars, blind comparison, and independent judge evidence. Use for high-value artifacts where correctness alone is insufficient.

Use cases: agent-building

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/taste-engine

What's inside

Skill name
taste-engine
Description
Turn subjective quality intent into executable hard gates, weighted rubrics, exemplars, anti-exemplars, blind comparison, and independent judge evidence. Use for high-value artifacts where correctness alone is insufficient.
License
MIT
Instructions
58 lines in SKILL.md · view on GitHub

Files (3)

  • SKILL.md
  • openai.yaml · agents/
  • portable-contracts.md · references/

SKILL.md (first lines)

# Taste Engine

## Outcome

Create a domain-specific Taste Profile and an evaluation loop that can reject polished mediocrity without pretending subjective judgment is deterministic.

When the SIS schema is unavailable, read `references/portable-contracts.md` before authoring the profile and keep all unrun evaluation `pending-runtime`.

## Use this skill when

- A high-value artifact must be persuasive, elegant, clear, resonant, or production-ready.
- “Premium,” “beautiful,” “world-class,” or similar intent needs an executable definition.
- Multiple candidates can be compared.
- A winning example should become reusable preference memory.

Do not use a taste rubric as a substitute for factual, security, accessibility, or artifact-native checks.

## Procedure

1. Identify the judgment context.
   - Name the artifact type, audience, decision, medium, production constraints, and what failure costs.
2. Write hard rejection gates.
   - Encode failures that invalidate an artifact regardless of polish.
   - Keep factual fabrication, unusable output, accessibility failure, and broken production constraints outside weighted averaging.
3. Define weighted dimensions.
   - Use observable descriptions of excellence and failure.
   - Make weights reflect the use case rather than generic aesthetic preference.
   - Confirm weights total approximately 1.0.
4. Ground the rubric.
   - Add reference exemplars and explain why each wins.
   - Add anti-exemplars and name the failure pattern.
   - Do not copy protected material into the profile; store references and rationales.
5. Generate candidates only when diversity is useful.
   - Change a real hypothesis, structure, or creative direction between candidates.
   - Avoid cosmetic variations that create fake choice.
6. Evaluate in layers.
   - Run deterministic artifact checks.
   - Use blind pairwise comparison where possible.
   - Add a domain critic and adversarial reviewer for consequential work.
   - Keep the producer from being the sole required judge.
7. Synthesize.
   - Let a separate owner combine winning properties without erasing the reason one candidate won.
8. Record preference evidence.
   - Store the winning reference, losing alternative, judgment rationale, judge identity or trace, and confidence.
   - Mark unrun or non-independent judging as pending.

## Evidence rules

- A judge result counts only when the evidence identifies it as producer-independent.
- Required taste lanes remain pending until the declared judge count passes.
- Taste cannot override a failed hard gate.
- Preference memory records why an artifact won, not just a score or adjective.

## Return

Return the Taste Profile, candidate strategy, deterministic gates, judge plan, evidence status, winning rationale, and unresolved disagreement. Include an Evidence Receipt only when an authorized runtime actually produced one; otherwise mark judging and receipt generation pending.

From frankxai/Starlight-Intelligence-System, MIT.

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Forks
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Open issues
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Last commit (this folder)
Oct 10, 2026 (19 days ago)
Last release
Jun 10, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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taste-engine skill. Agent Mart. https://agentmart.dev/skills/frankxai-taste-engine (last updated 2026-10-24)