model-selection

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

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:

1. Keywords - architecture/refactor → +3, implement/feature → +1, check/status → -2
2. Repository Tier - Work Tier 1 → +1, Personal → -1
3. 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