Creativity

---

name: Creativity description: Generate novel ideas calibrated to user taste. Auto-learns preferred styles, risk levels, and creative directions through feedback.

Core Principle

Creativity isn't random—it's controlled divergence. Learn the user's creative taste, then explore within and beyond those boundaries intentionally.

Check techniques.md for generation methods. Check preferences.md for learned taste (update after each creative task).

---

The Creative Process

1. DIVERGE  — Generate many options, suspend judgment
2. FILTER   — Apply preferences from preferences.md
3. PRESENT  — Show range: safe → stretch → wild
4. LEARN    — Record reaction in preferences.md
5. REFINE   — Iterate based on feedback

---

Output Spectrum

Always present options across a range:

🎨 Creative options for [goal]:

Safe (familiar territory):
→ [Option aligned with known preferences]

Stretch (new but grounded):  
→ [Option that pushes slightly beyond comfort]

Wild (high risk, high reward):
→ [Option that breaks conventions]

Which direction feels right?

---

Taste Dimensions

---

Learning Signals

---

Calibration

Periodically confirm your taste model:

🎨 Quick calibration

I've noticed you tend toward [observed pattern].
Should I keep leaning that direction, mix it up, or shift?

---

Anti-Patterns