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description: Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.

Selecting AI Models for Agents

Guidelines for choosing between sonnet and haiku models based on agent capabilities and task requirements.

When This Skill Loads

This Skill auto-loads when implementing agents or documenting model selection rationale.

Available Models

Sonnet (claude-sonnet-4-5)

Characteristics:

• Advanced reasoning capabilities
• Complex decision-making
• Deep pattern recognition
• Sophisticated analysis
• Multi-step orchestration
• Higher cost, slower performance

Use for: Complex, reasoning-intensive tasks

Haiku (claude-haiku-3-5)

Characteristics:

• Fast execution
• Straightforward tasks
• Pattern matching
• Simple decision-making
• Cost-effective
• Lower cost, faster performance

Use for: Simple, well-defined tasks

Decision Framework

Use Sonnet When Task Requires

✅ Advanced Reasoning

• Analyzing technical claims for subtle contradictions
• Distinguishing objective errors from subjective improvements
• Detecting false positives in validation findings
• Context-dependent decision-making
• Inferring user intent from ambiguous requests

✅ Complex Pattern Recognition

• Cross-referencing multiple documentation files
• Identifying conceptual duplications (not just verbatim)
• Detecting inconsistencies across architectural layers
• Understanding domain-specific patterns
• Recognizing semantic similarities

✅ Sophisticated Analysis

• Verifying factual accuracy against authoritative sources
• Assessing confidence levels (HIGH/MEDIUM/FALSE_POSITIVE)
• Evaluating code quality and architectural decisions
• Analyzing narrative flow and pedagogical structure
• Determining fix safety and impact

✅ Multi-Step Orchestration

• Coordinating complex validation workflows
• Managing dependencies between validation steps
• Iterative refinement processes
• Dynamic workflow adaptation
• Error recovery and retry logic

✅ Deep Web Research

• Finding and evaluating authoritative sources
• Comparing claims against official documentation
• Version verification across multiple registries
• API correctness validation
• Detecting outdated information

Use Haiku When Task Is

✅ Pattern Matching

• Extracting URLs from markdown files
• Finding code blocks by language
• Matching file naming patterns
• Regular expression searches
• Simple syntax validation

✅ Sequential Execution

• File existence checks
• URL accessibility validation
• Cache file reading/writing
• Date comparisons
• Status reporting

✅ Straightforward Validation

• Checking if files exist
• Verifying link format (contains .md)
• Counting lines or characters
• Comparing timestamps
• Simple YAML/JSON parsing

✅ No Complex Reasoning

• Tasks with clear pass/fail criteria
• No ambiguity or judgment required
• Deterministic outcomes
• No context analysis needed
• No trade-off decisions

✅ High-Volume Processing

• Checking hundreds of links
• Validating many files
• Batch operations
• Performance-critical tasks
• Cost-sensitive operations

Model Selection Matrix

Agent-Specific Examples

Sonnet Examples

docs-checker (Complex validation):

Copymodel: sonnet

Reasoning:

• Analyzes technical claims for contradictions
• Deep web research for fact verification
• Pattern recognition across multiple files
• Complex decision-making for criticality levels
• Multi-step validation orchestration

docs-fixer (Sophisticated analysis):

Copymodel: sonnet

Reasoning:

• Re-validates findings to detect false positives
• Distinguishes objective errors from subjective improvements
• Assesses confidence levels (HIGH/MEDIUM/FALSE_POSITIVE)
• Complex decision-making for fix safety
• Trust model analysis (when to trust checker)

docs-tutorial-checker (Pedagogical analysis):

Copymodel: sonnet

Reasoning:

• Evaluates narrative flow and learning progression
• Assesses hands-on element quality
• Analyzes visual completeness
• Determines tutorial type compliance
• Sophisticated quality judgment

Haiku Examples

docs-link-general-checker (Straightforward validation):

Copymodel: haiku

Reasoning:

• Pattern matching to extract URLs
• Sequential URL validation via requests
• File existence checks for internal references
• Cache management (read/write YAML, compare dates)
• Simple status reporting (working/broken/redirected)
• No complex reasoning required

docs-file-manager (File operations):

Copymodel: haiku

Reasoning:

• Straightforward file operations (move, rename, delete)
• Simple path manipulation
• Git history preservation (scripted commands)
• No complex decision-making
• Deterministic outcomes

Documenting Model Selection

Model Selection Justification Pattern

Include in agent documentation to explain model choice:

For Sonnet Agents:

Copy**Model Selection Justification**: This agent uses `model: sonnet` because it requires:

- [Reasoning capability 1 - e.g., "Advanced reasoning to analyze technical claims"]
- [Reasoning capability 2 - e.g., "Deep web research to verify facts"]
- [Reasoning capability 3 - e.g., "Pattern recognition across multiple files"]
- [Decision-making type - e.g., "Complex decision-making for criticality levels"]
- [Orchestration need - e.g., "Multi-step validation workflow orchestration"]

For Haiku Agents:

Copy**Model Selection Justification**: This agent uses `model: haiku` because it performs straightforward tasks:

- [Simple task 1 - e.g., "Pattern matching to extract URLs"]
- [Simple task 2 - e.g., "Sequential URL validation via web requests"]
- [Simple task 3 - e.g., "File existence checks"]
- [Simple task 4 - e.g., "Cache management (read/write/compare)"]
- [Simple task 5 - e.g., "Simple status reporting"]
- No complex reasoning or content generation required

Placement in Agent Files

Add justification near the top of agent file, after agent description:

Copy---
name: example-agent
description: Agent description here
model: sonnet
---

# Agent Name

**Model Selection Justification**: [justification here]

[Rest of agent documentation]

Cost and Performance Considerations

Sonnet Trade-offs

Costs:

• Higher per-token cost (~10x haiku)
• Slower response time
• More resource-intensive

Benefits:

• Higher quality reasoning
• Better context understanding
• More accurate decisions
• Handles ambiguity well

Use when: Quality and accuracy more important than cost/speed

Haiku Trade-offs

Benefits:

• Lower per-token cost (~10x cheaper)
• Faster response time
• Efficient for high-volume tasks

Limitations:

• Less sophisticated reasoning
• May struggle with ambiguity
• Better for deterministic tasks

Use when: Cost and speed more important than complex reasoning

Decision Checklist

Before selecting a model, ask:

1. Does the task require judgment calls?

   • Yes → Sonnet
   • No → Haiku

2. Are there multiple valid interpretations?

   • Yes → Sonnet
   • No → Haiku

3. Does it need deep analysis of context?

   • Yes → Sonnet
   • No → Haiku

4. Will it make complex decisions?

   • Yes → Sonnet
   • No → Haiku

5. Is it high-volume, low-complexity?

   • Yes → Haiku
   • No → Sonnet

6. Does cost matter more than quality?

   • Yes → Haiku
   • No → Sonnet

Common Mistakes

❌ Using Sonnet for Simple Tasks:

Copy# Overkill - use haiku
model: sonnet # Just checking if files exist

❌ Using Haiku for Complex Analysis:

Copy# Insufficient - use sonnet
model: haiku # Analyzing code quality and architecture

✅ Match Model to Task Complexity:

Copy# Simple pattern matching
model: haiku

# Complex reasoning
model: sonnet

Key Takeaways

• Sonnet = Complex reasoning, sophisticated analysis, multi-step orchestration
• Haiku = Simple tasks, pattern matching, straightforward validation
• Document rationale = Include model selection justification in agent files
• Consider trade-offs = Balance cost/speed vs quality/capability
• Match complexity = Use appropriate model for task requirements
• When in doubt = Choose sonnet for quality, haiku for speed/cost

Proper model selection ensures optimal performance, cost-effectiveness, and task completion quality.