vamseeachanta Model Selection Skill
Version: 1.0.0 Category: Optimization Triggers: Starting tasks, choosing Codex model, usage optimization
Quick Reference
Model Selection Decision Tree
NEW TASK
│
├── WORK REPO + COMPLEX → OPUS
├── WORK REPO + STANDARD → SONNET
├── PERSONAL + SIMPLE → HAIKU
└── DEFAULT → SONNET
Quick Selection Guide
| Model | Target % | Use For |
|---|---|---|
| OPUS | 30% | Architecture, multi-file refactoring (>5 files), security review |
| SONNET | 40% | Standard implementations, code review, documentation |
| HAIKU | 30% | Quick queries, status checks, simple operations |
Automated Model Suggestion
# Get model recommendation before each task
./scripts/monitoring/suggest_model.sh <repository> "<task description>"
# Examples:
./scripts/monitoring/suggest_model.sh digitalmodel "Design authentication architecture"
# → Recommends: OPUS (complexity score: 4)
./scripts/monitoring/suggest_model.sh digitalmodel "Implement user login"
# → Recommends: SONNET (complexity score: 1)
./scripts/monitoring/suggest_model.sh hobbies "Quick file check"
# → Recommends: HAIKU (complexity score: -3)
Complexity Scoring
Algorithm evaluates:
- Keywords - architecture/refactor → +3, implement/feature → +1, check/status → -2
- Repository Tier - Work Tier 1 → +1, Personal → -1
- Task Length - >15 words → +1, <5 words → -1
Score Mapping:
- Score ≥3: OPUS
- Score 0-2: SONNET
- Score <0: HAIKU
Repository Tiers
Work Repositories
Tier 1 (Production): 60% Opus, 30% Sonnet, 10% Haiku
- digitalmodel, energy, client-a
Tier 2 (Active): 30% Opus, 50% Sonnet, 20% Haiku
- assetutilities, worldenergydata
Tier 3 (Maintenance): 10% Opus, 30% Sonnet, 60% Haiku
- lng-a, client-d, OGManufacturing
Personal Repositories
Active: 20% Opus, 40% Sonnet, 40% Haiku Experimental: 5% Opus, 25% Sonnet, 70% Haiku Archive: 0% Opus, 20% Sonnet, 80% Haiku
Usage Monitoring
Check before starting work: https://Codex.ai/settings/usage
Alert Thresholds:
- Sonnet >70% → Switch to Opus/Haiku
- Session >80% → Batch work or wait
- Overall >80% → Defer non-critical
OPUS Use Cases
✅ Multi-file refactoring (>5 files) ✅ Architecture decisions ✅ Complex algorithm design ✅ Security-critical code review ✅ Cross-repository coordination ✅ Performance optimization strategies
SONNET Use Cases
✅ Standard feature implementation ✅ Code review (single PR) ✅ Documentation writing ✅ Test generation ✅ Bug fixing (standard complexity) ✅ Configuration updates
HAIKU Use Cases
✅ File existence checks ✅ Simple grep/search operations ✅ Quick status updates ✅ Log analysis (pattern matching) ✅ Template generation ✅ Format validation
Emergency Protocols
If Sonnet >80%
⛔ STOP using Sonnet immediately
✅ Switch to Opus for critical work
✅ Switch to Haiku for everything else
📅 Defer non-urgent work to Tuesday
If Session >80%
⏸️ Pause AI tasks
⏰ Wait for session reset (~3-4 hours)
📦 Batch work for next session
Full Reference
See: @docs/AI_MODEL_SELECTION_AUTOMATION.md See: @docs/CLAUDE_MODEL_SELECTION_QUICK_REFERENCE.md
Use this when starting tasks, selecting models, or optimizing AI usage.