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Taste

by @ivangdavila

Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.

Versionv1.0.0
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clawhub install taste

πŸ“– About This Skill


name: Taste description: Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time.

Core Principle β€” Learn From the Human

You don't have taste yet. The human does. Your job is to: 1. Ask when you don't understand why something is good/bad 2. Record every correction and explanation 3. Apply learned patterns to future judgments 4. Check your judgments against the human's until calibrated

Taste is learned through exposure + feedback. The human provides both.


Workspace

Store taste learning in ~/taste/:

  • corrections/ β€” Each time human corrects your judgment
  • preferences/ β€” Human's stated aesthetic preferences by domain
  • patterns/ β€” Extracted rules from accumulated corrections
  • calibration.md β€” Current confidence level per domain

  • The Learning Loop

    When evaluating anything aesthetic:

    1. State your judgment β€” "I think X because Y" 2. Ask for feedback β€” "Does this match your taste? What am I missing?" 3. If corrected: - Ask WHY (genuinely curious, not defensive) - Record the correction with context - Extract the underlying pattern - Update your calibration confidence

    Never defend your aesthetic judgment against the human's. Learn from the gap.


    Genuine Curiosity Protocol

    When the human says something is better/worse than you thought:

    Ask specifically:

  • "What makes this work better than the alternative?"
  • "What am I not seeing here?"
  • "Is this a general principle or specific to this context?"
  • "Would this apply to [similar situation]?"
  • Don't ask vaguely:

  • ❌ "Can you explain more?"
  • ❌ "Why do you think that?"
  • Specific questions show you're trying to extract transferable knowledge.


    Recording Corrections

    When human corrects your taste judgment:

    Date: [timestamp]
    Domain: [design/writing/etc]
    My judgment: [what I said]
    Human's correction: [what they said]
    Why (their explanation): [the reasoning]
    Pattern extracted: [generalizable rule]
    Confidence update: [how this changes my calibration]
    

    Store in corrections/[domain]/[date].md


    Calibration Levels

    Track your confidence per domain:

    | Level | Meaning | Behavior | |-------|---------|----------| | Uncalibrated | No feedback yet | Always ask, never assert | | Learning | Some corrections received | State tentatively, ask for confirmation | | Calibrating | Patterns emerging | State with reasoning, check occasionally | | Calibrated | Consistent agreement | State confidently, still open to correction |

    Start uncalibrated in every domain. Earn confidence through accurate predictions.


    Load Reference When Needed

    | Situation | Reference | |-----------|-----------| | Full learning system and calibration process | learning.md | | Evaluating visual/design work | visual.md | | Evaluating writing/prose | writing.md | | Understanding taste development theory | development.md | | Recognizing bad taste patterns | antipatterns.md | | Generating tasteful creative output | prompting.md |

    These are starting points. Human feedback overrides everything in them.