Prompt file imported from rhixecompany/sandbox (.github/prompts/research/model-recommendation/model-recommendation.prompt.md). Fill in {{filePath}}, {{subscriptionTier}}, {{priorityFactor}} before use. Copyright stays with the author.

Table of Contents

Goal

Analyzes chatmode or prompt files and recommends optimal AI models based on task complexity, capabilities, and cost-efficiency.

Context

Phases

Table of Contents

• Goal

• Context

• Inputs

• Outputs

• Rules

• Phases

  • Phase 1: Intake
  • Phase 2: Execute
  • Phase 3: Verify
  • Phase 4: Hand off

• Mission

• Scope & Preconditions

• Prompt Variables

• Workflow

  • 1. File Analysis Phase

• Recommendation: Add Model Specification

• Current Model Assessment

• Output Expectations

  • Report Structure

• [File SummaryDescription: [from frontmatter] Mode: ask

• Task Analysis

  • Task Complexity
  • Task Category
  • Key Characteristics

• Model Recommendation>

  • 🏆 Primary Recommendation: [Model Name]

• Auto Model Selection Assessment

• Implementation Guidance

  • Frontmatter Update
  • Model Selection in VS CodeTo Use Recommended Model:
  • Tool Alignment Verification

• Deprecation Notices

• Additional Considerations

  • Subscription Tier Recommendations
  • Priority Factor Adjustments
  • Long-Term Model Strategy

• Quick Reference

  • Output Quality Standards

• Quality Assurance

• Advanced Use Cases

  • Analyzing Multiple Files
  • Comparative Analysis
  • Migration Planning

• Examples

  • Example 1: Simple Formatting Task
  • Example 2: Complex Architecture Review
  • Example 3: Django Expert Mode
  • Example 4: Free Tier User with Planning Mode

• Knowledge Base

  • Model Multiplier Cost Reference
  • Model Changelog & Deprecations (October 2025)

• Template References

• Personas

• Personality

• Best Practices

• Verification Checklist

• Dependencies

• Subgoals

• Skills Required

• MCP Servers & Tools

• Tasks

• Hooks

• Scripts

• Goal

• Context

• Inputs

• Outputs

• Rules

• Phases

• Phase 1: Intake

• Phase 2: Execute

• Phase 3: Verify

• Phase 4: Hand off

• Mission

• Scope & Preconditions

• Prompt Variables

• Workflow

• 1. File Analysis Phase

• Recommendation: Add Model Specification

• Current Model Assessment

• Output Expectations

• Report Structure

• [File SummaryDescription: [from frontmatter] Mode: ask

• Task Analysis

• Task Complexity

• Task Category

• Key Characteristics

• Model Recommendation>

• 🏆 Primary Recommendation: [Model Name]

• Auto Model Selection Assessment

• Implementation Guidance

• Frontmatter Update

• Model Selection in VS CodeTo Use Recommended Model:

• Tool Alignment Verification

• Deprecation Notices

• Additional Considerations

• Subscription Tier Recommendations

• Priority Factor Adjustments

• Long-Term Model Strategy

• Quick Reference

• Output Quality Standards

• Quality Assurance

• Advanced Use Cases

• Analyzing Multiple Files

• Comparative Analysis

• Migration Planning

• Examples

• Example 1: Simple Formatting Task

• Example 2: Complex Architecture Review

• Example 3: Django Expert Mode

• Example 4: Free Tier User with Planning Mode

• Knowledge Base

• Model Multiplier Cost Reference

• Model Changelog & Deprecations (October 2025)

• Template References

• Personas

• Personality

• Best Practices

• Verification Checklist

• Dependencies

• Subgoals

• Skills Required

• MCP Servers & Tools

• Tasks

• Hooks

• Scripts

Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency.

Use when you need to work on the current workspace or task.

Inputs

• The current workspace, repo, or document state.
• The specific request, diff, spec, or files provided by the user.
• Any prompt variables, paths, or constraints named in the original instructions.

Outputs

• A complete result that matches the prompt's purpose.
• A concise verification note when the task benefits from one.

Rules

Core rules: templates/rules-core.md

• Follow the prompt literally and prefer evidence from the current workspace.
• Keep the response structured, deterministic, and easy to act on.
• Avoid changing unrelated files or adding unnecessary scope.
• If something is unclear, state the assumption instead of guessing.

Phase 1: Intake

• Read the request and identify the exact scope.
• Locate the relevant files, diffs, or references.

Phase 2: Execute

• Perform the requested work with the smallest safe change set.
• Keep the steps explicit and reproducible.

Phase 3: Verify

• Check the result against the goal, rules, and inputs.
• Confirm the output is usable and complete.

Phase 4: Hand off

• Return the final artifact or findings .
• Stop once the requested result is delivered.

Mission

Analyze .agent.md or .prompt.md files to understand their purpose, complexity, and required capabilities, then recommend the most suitable AI model(s) from GitHub Copilot's available options. Provide rationale based on task characteristics, model strengths, cost-efficiency, and performance trade-offs.

Scope & Preconditions

• Input: Path to a .agent.md or .prompt.md file- Available Models: GPT-4.1, GPT-5, GPT-5 mini, GPT-5 Codex, Claude Sonnet 3.5, Claude Sonnet 4, Claude Sonnet 4.5, Claude Opus 4.1, Gemini 2.5 Pro, Gemini 2.0 Flash, Grok Code Fast 1, o3, o4-mini (with deprecation dates)- Model Auto-Selection: Available in VS Code (Sept 2025+) - selects from GPT-4.1, GPT-5 mini, GPT-5, Claude Sonnet 3.5, Claude Sonnet 4.5 (excludes premium multipliers

1)- Context: GitHub Copilot subscription tiers (Free: 2K completions + 50 chat/month with 0x models only; Pro: unlimited 0x + 1000 premium/month; Pro+: unlimited 0x + 5000 premium/month)

Prompt Variables

Required:- {{filePath}} - Absolute or workspace-relative path to the file to analyzeOptional:- {{subscriptionTier}} - User's Copilot subscription tier (Free, Pro, Pro+) - defaults to Pro- {{priorityFactor}} - Optimization priority (Speed, Cost, Quality, Balanced) - defaults to Balanced

Workflow

1. File Analysis Phase

Read and Parse File: Full content:

Recommendation: Add Model Specification

Current frontmatter: ```yaml---description: "..." tools: [...]---```Recommended frontmatter: ```yaml---description: "..." model: "[Recommended Model Name]" tools: [...]---```Rationale: [Explanation of why this model is optimal for this task]If file already specifies a model:markdown

Current Model Assessment

Specified model: [Current Model] (Multiplier: [X]x) Recommendation: [Keep current model | Consider switching to [Recommended Model]] Full content:

Output Expectations

Report Structure

Generate a structured markdown report with the following sections:```markdown# AI Model Recommendation ReportFile Analyzed: [file path] File Type: [chatmode | prompt] Analysis Date: [YYYY-MM-DD] Subscription Tier: [Free | Pro | Pro+]---

File SummaryDescription: [from frontmatter] Mode: [ask

| edit | agent] Tools: [tool list] Current Model: [specified model or "Not specified"]

Task Analysis

Task Complexity

• Level: [Simple | Moderate | Complex | Advanced]
• Reasoning Depth: [Basic | Intermediate | Advanced | Expert]
• Context Requirements: [Small | Medium | Large | Very Large]
• Code Generation: [Minimal | Moderate | Extensive]
• Multi-Modal: [Yes | No]

Task Category

[Primary category from 8 categories listed in Workflow Phase 1]

Key Characteristics

• Characteristic 1: [explanation]
• Characteristic 2: [explanation]
• Characteristic 3: [explanation]

Model Recommendation>

🏆 Primary Recommendation: [Model Name]

Multiplier: [X]x ([cost implications for subscription tier]) Strengths: Full content:

Auto Model Selection Assessment

Suitability: [Recommended | Not Recommended | Situational]

Explanation of whether auto-selection is appropriate for this task

Rationale:

• [Reason 1]
• [Reason 2]

Manual Override Scenarios:

• [Scenario where user should manually select model]
• [Scenario where user should manually select model]

Implementation Guidance

Frontmatter Update

[Provide specific code block showing recommended frontmatter change]

Model Selection in VS CodeTo Use Recommended Model:

1. Open Copilot Chat
2. Click model dropdown (currently shows "[current model or Auto]")
3. Select [Recommended Model Name]
4. [Optional: When to switch back to Auto]Keyboard Shortcut: Cmd+Shift+P → "Copilot: Change Model"

Tool Alignment Verification

[Check results: Are specified tools compatible with recommended model?]✅ Compatible Tools: [list] ⚠️ Potential Limitations: [list if any]

Deprecation Notices

[If applicable, list any deprecated models in current configuration]⚠️ Deprecated Model in Use: [Model Name] (Deprecation date: [YYYY-MM-DD])Migration Path:- Current: [Deprecated Model]- Replacement: [Recommended Model]- Action Required: Update model: field in frontmatter by [date]- Behavioral Changes: [any expected differences]

Additional Considerations

Subscription Tier Recommendations

[Specific advice based on Free/Pro/Pro+ tier]

Priority Factor Adjustments

[If user specified Speed/Cost/Quality/Balanced, explain how recommendation aligns]

Long-Term Model Strategy

[Advice for when to re-evaluate model selection as file evolves]---

Quick Reference

TL;DR: Use [Primary Model] for this task due to [one-sentence rationale]. Cost: [X]x multiplier. Use [Primary Model] for this task due to [one-sentence rationale]. Cost: [X]x multiplier.One-Line Update: ```yaml model: "[Recommended Model Name]" ``````

Output Quality Standards

• Specific: Tie all recommendations directly to file content, not generic advice
• Actionable: Provide exact frontmatter code, VS Code steps, clear migration paths
• Contextualized: Consider subscription tier, priority factor, deprecation timelines
• Evidence-Based: Reference model capabilities from Context7 documentation when available
• Balanced: Present trade-offs honestly (speed vs. quality vs. cost)
• Up-to-Date: Flag deprecated models, suggest current alternatives

Quality Assurance

• File successfully read and parsed
• Frontmatter extracted correctly (or noted if missing)

Advanced Use Cases

Analyzing Multiple Files

If user provides multiple files:1. Analyze each file individually2. Generate separate recommendations per file3. Provide summary table comparing recommendations4. Note any patterns (e.g., "All debug-related modes benefit from Claude Sonnet 4.5")

Comparative Analysis

If user asks "Which model is better between X and Y for this file?":1. Focus comparison on those two models only2. Use side-by-side table format3. Declare a winner with specific reasoning4. Include cost comparison for subscription tier

Migration Planning

If file specifies a deprecated model:

Examples

Example 1: Simple Formatting Task

File: format-code.prompt.md Content: "Format Python code with Black style, add type hints" Recommendation: GPT-5 mini (0x multiplier, fastest, sufficient for repetitive formatting) Alternative: Grok Code Fast 1 (0.25x, even faster, preview feature) Rationale: Task is simple and repetitive; premium reasoning not needed; speed prioritized

Example 2: Complex Architecture Review

File: architect.agent.md Content: "Review system design for scalability, security, maintainability; analyze trade-offs; provide ADR-level recommendations" Recommendation: Claude Sonnet 4.5 (1x multiplier, expert reasoning, excellent for architecture) Alternative: Claude Opus 4.1 (10x, use for very large codebases

500K tokens) Rationale: Requires deep reasoning, architectural expertise, design pattern knowledge; Sonnet 4.5 excels at this

Example 3: Django Expert Mode

File: django.agent.md Content: "Django 5.x expert with ORM optimization, async views, REST API design; uses context7 for up-to-date Django docs" Recommendation: GPT-5 (1x multiplier, advanced reasoning, excellent code quality) Alternative: Claude Sonnet 4.5 (1x, alternative perspective, strong with frameworks) Rationale: Domain expertise + context7 integration benefits from advanced reasoning; 1x cost justified for expert mode

Example 4: Free Tier User with Planning Mode

File: plan.agent.md Content: "Research and planning mode with read-only tools (search, fetch, githubRepo)" Subscription: Free (2K completions + 50 chat requests/month, 0x models only) Recommendation: GPT-4.1 (0x, balanced, included in Free tier) Alternative: GPT-5 mini (0x, faster but less context) Rationale: Free tier restricted to 0x models; GPT-4.1 provides best balance of quality and context for planning tasks

Knowledge Base

Model Multiplier Cost Reference

Model Changelog & Deprecations (October 2025)

Template References

Detailed section templates in templates/:- context7_query_templates.md- current_model_assessment.md- knowledge_base.md- model_recommendation.md- quality_assurance.md- workflow.md

Personas

See templates/personas.md for shared persona templates.

Personality

See templates/personality.md for shared personality guidelines.

• Tone: Direct, practical, actionable
• Style: Structured with clear steps and verification
• Avoid: Ambiguity, assumptions, scope creep
• Encourage: Evidence-based decisions, minimal changes

Best Practices

See templates/best-practices.md for cross-cutting best practices.

1. DRY — Reference shared templates instead of duplicating content.
2. Structured output — Use clear sections with consistent heading levels.
3. Verification gates — Always verify before claiming completion.
4. Minimal changes — Fix root cause, not symptoms.

Verification Checklist

Dependencies

See templates/deps-core.md for shared dependency patterns.

Subgoals

1. Prepare — Understand requirements and prerequisites.
2. Execute — Follow structured workflow with incremental progress.
3. Verify — Confirm output meets requirements and standards.
4. Document — Record results, decisions, and lessons learned.

Skills Required

See templates/skills-table-core.md for shared skills table.

MCP Servers & Tools

The following MCP servers and tools are available for this task. Use them in preference to native equivalents per MCP-first tooling policy.

| ast-grep | AST-based code search and replace | | filesystem | File read/write operations | | sequential-thinking | Structured reasoning for complex problems | | fetch | Web page content extraction | | playwright | Browser automation for interactive pages | | github | GitHub API operations |

Tasks

• Understand requirements and scope
• Plan approach and identify resources
• Execute work incrementally
• Verify against acceptance criteria
• Document results and decisions

Hooks

Shared workspace hooks run around this prompt's execution — see .github/hooks/README.md: session-logger, session-auto-commit, governance-audit, pre-exec-validate.sh, post-exec-state-log.py.

Scripts

Prompt-library tooling (see .enhance/):

• .enhance/analyze_prompts.py — prompt-library analyzer (Phase 5/7 gate)
• .enhance/verify_phase3.py, .enhance/fix_class_e.py, .enhance/fix_frontmatter_plan.py — Class C–E repair/verify tooling
• .github/hooks/* — hook implementations referenced in the Hooks section

# Prompt template
Execute the workflow defined in this file.