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Guide AI model selection based on task complexity, cost constraints, and latency requirements
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Basé sur la classification professionnelle SOC
| name | model-selection |
| version | 1.0.0 |
| category | ai |
| description | Guide AI model selection based on task complexity, cost constraints, and latency requirements |
| type | reference |
| capabilities | [] |
| requires | [] |
| see_also | [] |
| tags | [] |
| scripts_exempt | true |
Version: 1.0.0 Category: Optimization Triggers: Starting tasks, choosing Claude model, usage optimization
NEW TASK
│
├── WORK REPO + COMPLEX → OPUS
├── WORK REPO + STANDARD → SONNET
├── PERSONAL + SIMPLE → HAIKU
└── DEFAULT → SONNET
| 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 |
# 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)
Algorithm evaluates:
Score Mapping:
Tier 1 (Production): 60% Opus, 30% Sonnet, 10% Haiku
Tier 2 (Active): 30% Opus, 50% Sonnet, 20% Haiku
Tier 3 (Maintenance): 10% Opus, 30% Sonnet, 60% Haiku
Active: 20% Opus, 40% Sonnet, 40% Haiku Experimental: 5% Opus, 25% Sonnet, 70% Haiku Archive: 0% Opus, 20% Sonnet, 80% Haiku
Check before starting work: https://claude.ai/settings/usage
Alert Thresholds:
✅ Multi-file refactoring (>5 files) ✅ Architecture decisions ✅ Complex algorithm design ✅ Security-critical code review ✅ Cross-repository coordination ✅ Performance optimization strategies
✅ Standard feature implementation ✅ Code review (single PR) ✅ Documentation writing ✅ Test generation ✅ Bug fixing (standard complexity) ✅ Configuration updates
✅ File existence checks ✅ Simple grep/search operations ✅ Quick status updates ✅ Log analysis (pattern matching) ✅ Template generation ✅ Format validation
⛔ STOP using Sonnet immediately
✅ Switch to Opus for critical work
✅ Switch to Haiku for everything else
📅 Defer non-urgent work to Tuesday
⏸️ Pause AI tasks
⏰ Wait for session reset (~3-4 hours)
📦 Batch work for next session
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.
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Guide AI model selection based on task complexity, cost constraints, and latency requirements
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