refactor: split emergent-patterns into focused problem-solving skills

**Restructure:**
- Split detecting-emergent-patterns (216 lines) into 5 focused skills (~60 lines each)
- Move tracing-lineages to skills/research/ (better category fit)
- Create skills/problem-solving/ category

**New problem-solving skills:**
- simplification-cascades: Find one insight that eliminates multiple components
- collision-zone-thinking: Force unrelated concepts together for breakthroughs
- meta-pattern-recognition: Spot patterns in 3+ domains
- inversion-exercise: Flip assumptions to reveal alternatives
- scale-game: Test at extremes to find fundamental limits
- when-stuck: Dispatch skill that maps stuck-symptoms to techniques

**Improvements:**
- Symptom-based when_to_use (what you'd actually think/feel)
- Quick reference tables for rapid scanning
- Pattern-based triggers (not exact phrase matching)
- Cross-references from brainstorming for discoverability

Each skill now: scannable in 30 seconds, applicable immediately, focused on one technique.

skills/problem-solving/scale-game/SKILL.md

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+---
+name: Scale Game
+description: Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
+when_to_use: Unsure if approach will scale. Edge cases unclear. Want to validate architecture. "Will this work at production scale?" Need to find fundamental limits.
+version: 1.0.0
+---
+
+# Scale Game
+
+## Overview
+
+Test your approach at extreme scales to find what breaks and what surprisingly survives.
+
+**Core principle:** Extremes expose fundamental truths hidden at normal scales.
+
+## Quick Reference
+
+| Scale Dimension | Test At Extremes | What It Reveals |
+|-----------------|------------------|-----------------|
+| Volume | 1 item vs 1B items | Algorithmic complexity limits |
+| Speed | Instant vs 1 year | Async requirements, caching needs |
+| Users | 1 user vs 1B users | Concurrency issues, resource limits |
+| Duration | Milliseconds vs years | Memory leaks, state growth |
+| Failure rate | Never fails vs always fails | Error handling adequacy |
+
+## Process
+
+1. **Pick dimension** - What could vary extremely?
+2. **Test minimum** - What if this was 1000x smaller/faster/fewer?
+3. **Test maximum** - What if this was 1000x bigger/slower/more?
+4. **Note what breaks** - Where do limits appear?
+5. **Note what survives** - What's fundamentally sound?
+
+## Examples
+
+### Example 1: Error Handling
+**Normal scale:** "Handle errors when they occur" works fine
+**At 1B scale:** Error volume overwhelms logging, crashes system
+**Reveals:** Need to make errors impossible (type systems) or expect them (chaos engineering)
+
+### Example 2: Synchronous APIs
+**Normal scale:** Direct function calls work
+**At global scale:** Network latency makes synchronous calls unusable
+**Reveals:** Async/messaging becomes survival requirement, not optimization
+
+### Example 3: In-Memory State
+**Normal duration:** Works for hours/days
+**At years:** Memory grows unbounded, eventual crash
+**Reveals:** Need persistence or periodic cleanup, can't rely on memory
+
+## Red Flags You Need This
+
+- "It works in dev" (but will it work in production?)
+- No idea where limits are
+- "Should scale fine" (without testing)
+- Surprised by production behavior
+
+## Remember
+
+- Extremes reveal fundamentals
+- What works at one scale fails at another
+- Test both directions (bigger AND smaller)
+- Use insights to validate architecture early