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

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)

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)

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

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

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)")

⚠️ Coding agents must NOT have model: in frontmatter

• The model: property is only valid for VS Code agents (files without -coding-agent suffix)

• On GitHub.com, the model: property in *-coding-agent.agent.md files causes a hard CAPIError: 400 The requested model is not supported error (confirmed by experiment)

• The GitHub docs say this property is "ignored" but in practice it prevents the agent from running

• Apply model selection only to the corresponding VS Code agent file (e.g., developer.agent.md )

Model Selection Process

When selecting or changing a model:

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

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

Check category-specific performance

• Look up relevant benchmarks in ai-model-reference.md

• Compare top 3-5 performers in that category

Consider cost vs frequency

• High-frequency agents → favor lower multipliers (0.33x, 0x)

• Critical accuracy agents → favor best performer regardless of cost

Verify availability

• Confirm model is listed in "Available Models" section

• Check it's available for VS Code (required)

Document your reasoning

• Include benchmark scores in proposal

• Explain trade-offs made

Example Model Selection

Scenario: Selecting model for Quality Engineer agent

• Primary tasks: Define test plans following specific template format

• Key categories: Instruction Following (critical), Reasoning (important)

• 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)

• Decision: Gemini 3 Pro (balance of performance and cost)

• 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