5 techniques for different problem types. Use when stuck or facing complex challenges.
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Version: 1.0.0 Use When: Stuck, need breakthrough, or evaluating approaches
techniques[5]{name,when,approach}:
Simplification Cascades,Complexity spiraling / 5+ attempts failed,Strip to minimal → rebuild
Collision-Zone Thinking,Innovation blocks / need breakthrough,Combine unrelated concepts
Meta-Pattern Recognition,Same issue across domains,Find underlying pattern
Inversion Exercise,Forced into "only way" thinking,Ask "what if opposite?"
Scale Game,Production readiness unclear,Test at 10x / 100x / 1000x
When: Complexity spiraling, 5+ implementations tried
Process:
1. Remove ALL features except core
2. Make it work with hardcoded values
3. Add ONE thing back
4. Repeat until issue appears
5. Fix at that layer
Example:
Problem: Auth + caching + retry + logging all broken
Simplify:
1. Remove retry, logging, caching → just auth
2. Auth works? Yes → add caching
3. Caching breaks it → found the issue
4. Fix caching layer
5. Re-add retry, logging
When: Need creative breakthrough, conventional approaches failed
Process:
1. List unrelated domains
2. Find principles from each
3. Combine into novel solution
Example:
Problem: Users abandoning checkout
Domains: Gaming + Psychology + Logistics
Collision:
- Gaming: Progress bars, achievements
- Psychology: Loss aversion
- Logistics: Just-in-time delivery
Solution: "Your items are reserved for 10 min" +
progress indicator +
"3 people viewing this item"
When: Same issue keeps appearing in different forms
Process:
1. List all similar issues
2. Find what they share
3. Fix the meta-pattern
Example:
Issues:
- Users table query slow
- Orders table query slow
- Products table query slow
Meta-pattern: All queries filter by date without index
Fix: Add date indexes to all tables
When: Stuck in "only way" thinking
Process:
1. State current assumption
2. Ask: "What if the opposite?"
3. Explore inverted approach
Example:
Assumption: "We need to cache API responses"
Inversion: "What if we never cache?"
→ Forces real-time design
→ Discovers: Most data doesn't change
→ Solution: Cache-first with invalidation
(opposite of assumed API-first)
When: Production readiness unclear
Process:
1. Test at 10x current load
2. Test at 100x
3. Test at 1000x
4. Find breaking point
5. Design for 10x actual need
Example:
Current: 100 users/day
Scale test:
- 1,000: Works fine
- 10,000: DB connection pool exhausted
- 100,000: Memory OOM
Breaking point: 10,000 users
Design for: 1,000 (10x buffer)
Fix: Connection pooling + memory optimization
decision[5]{symptom,technique,model}:
"Tried everything",Simplification Cascades,sonnet
"Need creative idea",Collision-Zone Thinking,opus
"Keeps happening",Meta-Pattern Recognition,sonnet
"No other way",Inversion Exercise,sonnet
"Will it scale?",Scale Game,sonnet
Stuck → Simplify first
Creative block → Collision zones
Recurring issues → Meta-patterns
Tunnel vision → Invert assumptions
Scaling fears → Scale game
Invoke: Use when conventional debugging fails or need breakthrough.
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