Agent Model Selection Skill

Purpose

Provides data-driven guidance for selecting the most appropriate language model when creating or modifying agent definitions.

When to Use

• When creating a new agent and need to assign a model
• When modifying an existing agent’s model assignment
• When troubleshooting agent performance issues related to model capabilities
• When optimizing costs across the agent ecosystem

Reference Data

Always consult docs/ai-model-reference.md for:

• Current performance benchmarks by category (Coding, Reasoning, Language, Instruction Following, etc.)
• Model availability in GitHub Copilot Pro
• Premium request multipliers (cost)
• Recommended model assignments by agent type
• Task-based guidance and tutorials (via external links with descriptions)

This reference is updated periodically with latest benchmark data.

Critical Learnings

1. Use task-specific benchmarks, not overall scores

   • Different models excel at different tasks
   • Example: GPT-5.2-Codex excels in Coding while Claude Sonnet 4.5 is better for Language (76.00)

2. Claude Sonnet 4.5 has poor Instruction Following (score: 23.52)

   • Unsuitable for agents that follow templates (Task Planner, Quality Engineer)
   • Use Gemini models instead for structured output (scores: 65-75)

3. Gemini 3 Flash offers best value for many tasks

   • 0.33x premium multiplier (cost-effective)
   • Strong Instruction Following (74.86)
   • Good Language performance (84.56)
   • Ideal for: Task Planner, Release Manager, high-frequency agents

4. GPT-5.2-Codex is the latest coding model

   • Latest generation Codex model (improved over 5.1 Codex Max)
   • Specialized for agentic coding tasks
   • Primary choice for Developer agent
   • Also solid for Code Reviewer

5. Always verify model availability

   • Check against official GitHub Copilot documentation
   • Model names must match exactly (case-sensitive)
   • Include “(Preview)” suffix for preview models (e.g., “Gemini 3 Pro (Preview)”)

Model Selection Process

When selecting or changing a model:

1. Identify the agent’s primary task categories (from ai-model-reference.md)

   • Coding, Reasoning, Language, Instruction Following, etc.

2. Check category-specific performance

   • Look up relevant benchmarks in ai-model-reference.md
   • Compare top 3-5 performers in that category

3. Consider cost vs frequency

   • High-frequency agents → favor lower multipliers (0.33x, 0x)
   • Critical accuracy agents → favor best performer regardless of cost

4. Verify availability

   • Confirm model is listed in “Available Models” section
   • Check it’s available for VS Code (required)

5. Document your reasoning

   • Include benchmark scores in proposal
   • Explain trade-offs made

Example Model Selection

Scenario: Selecting model for Quality Engineer agent

1. Primary tasks: Define test plans following specific template format
2. Key categories: Instruction Following (critical), Reasoning (important)
3. Benchmark lookup (from ai-model-reference.md):
   • Gemini 3 Flash: Instruction Following 74.86, 0.33x cost ✅
   • Gemini 3 Pro: Instruction Following 65.85, 1x cost ✅
   • Claude Sonnet 4.5: Instruction Following 23.52 ❌ (disqualified)
4. Decision: Gemini 3 Pro (balance of performance and cost)
5. Rationale: Strong instruction following (65.85), reasonable cost (1x), good for template-based work

When to Update Model Assignments

Reassess models when:

• New benchmark data shows significant performance changes
• Agent is underperforming its tasks consistently
• New models are released with better performance
• Cost optimization is needed
• ai-model-reference.md is updated with new data

Key Principles

• Task-specific benchmarks matter more than overall scores
• Balance cost with performance based on agent frequency and criticality
• Always verify availability against official documentation
• Document your rationale for model selection decisions