model-selection-guide

name	model-selection-guide
description	Guide for selecting the right Claude model (Haiku, Sonnet, Opus) for different tasks. Use when starting a new task, planning implementation, or optimizing costs. Helps choose between fast/cheap Haiku and powerful Sonnet/Opus. (project)
model	haiku

Model Selection Guide

This skill provides guidance on which Claude model to use for different tasks.

Model Capabilities Overview

Model	Cost	Speed	Best For
Haiku	$	Fast	Well-specified implementation, pattern following
Sonnet	$$	Balanced	Complex logic, architectural decisions, debugging
Opus	$$$	Powerful	Novel architectures, major refactoring

Decision Tree

Is this well-specified in SPEC.md?
├─ YES → Does it follow an existing pattern?
│   ├─ YES → Use Haiku
│   └─ NO → Is it complex logic?
│       ├─ YES → Use Sonnet
│       └─ NO → Use Haiku
│
└─ NO → Is it a major architectural decision?
    ├─ YES → Use Opus
    └─ NO → Use Sonnet

Use Haiku (Fast & Cheap) for:

Implementation from Specs

• Implementing structs/types defined in SPEC.md
• Creating HTTP handlers following cursor-api-patterns
• Writing Go code following go-best-practices
• Building React components with clear requirements

Writing Tests

• Table-driven tests from test-plan.md
• Unit tests for well-specified functions
• Integration tests following patterns

Pattern Following

• Adding pagination to endpoints
• Implementing Basic Auth
• Error handling patterns
• JSON marshaling

Simple Refactoring

• Extract helper functions
• Rename variables/functions
• Format code

Use Sonnet (Balanced) for:

Complex Logic

• Poisson distribution event generation
• Rate limiting algorithms
• Caching strategies
• Concurrent worker pools

Architectural Decisions

• Service integration patterns
• Database schema design
• API versioning strategy

Multi-File Changes

• Feature implementation spanning multiple files
• Refactoring across packages
• Adding middleware layers

Debugging

• Race condition investigation
• Performance bottleneck identification
• Integration test failures

Use Opus (Powerful) for:

Novel Architectures

• Designing new major features not in specs
• Complex refactoring across all services
• Performance optimization strategy

Cross-Service Integration

• End-to-end feature implementation
• Contract negotiation between services
• Breaking API changes

Strategic Planning

• Choosing technology stack
• Migration planning
• Security architecture review

Cost Optimization Strategy

Session-Based Approach

1. Planning Session (Sonnet/Opus): Create work items, design docs
2. Implementation Session (Haiku): Write tests and code from specs
3. Integration Session (Sonnet): Wire up components, debug

Cost Comparison

All Sonnet: ~$4.25 per feature Hybrid Approach: ~$1.55 per feature (64% savings)

Task-to-Model Mapping

cursor-sim Tasks

Task	Model	Rationale
Initialize Go Project	Sonnet	Project structure decisions
CLI Flag Parsing	Haiku	Standard flag/JSON parsing
Developer Profile Generator	Haiku	Struct from SPEC.md
Event Generation Engine	Sonnet	Complex timing logic
In-Memory Storage	Haiku	Standard sync.Map patterns
REST API Handlers	Haiku	Following cursor-api-patterns
Wire Up Main	Sonnet	Integration and error handling

When in Doubt

Default to Sonnet if:

• You're unsure of the complexity
• It's the first time implementing this pattern
• The task involves multiple services

Switch to Haiku once:

• The pattern is established
• You have clear acceptance criteria
• The task is well-isolated

Summary

The comprehensive specs in this project make it ideal for Haiku usage. Use Sonnet/Opus for planning and complex logic, then leverage Haiku for well-specified implementation work.