Skill: ivangdavila/taste (version 1.0.0)

--- name: Taste description: Develop refined aesthetic judgment by learning from human feedback, asking genuine questions about quality, and calibrating over time. --- ## Core Principle — 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/** — Each time human corrects your judgment - **preferences/** — Human's stated aesthetic preferences by domain - **patterns/** — Extracted rules from accumulated corrections - **calibration.md** — Current confidence level per domain --- ## The Learning Loop When evaluating anything aesthetic: 1. **State your judgment** — "I think X because Y" 2. **Ask for feedback** — "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.


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