model-recommendation — Рекомендации моделей для GitHub Copilot

--- name: model-recommendation description: 'Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency' --- # AI Model Recommendation for Copilot Chat Modes and Prompts ## 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) ## Inputs Required: - `${input:filePath:Path to .agent.md or .prompt.md file}` - Absolute or workspace-relative path to the file to analyze Optional: - `${input:subscriptionTier:Pro}` - User's Copilot subscription tier (Free, Pro, Pro+) - defaults to Pro - `${input:priorityFactor:Balanced}` - Optimization priority (Speed, Cost, Quality, Balanced) - defaults to Balanced ## Workflow ### 1. File Analysis Phase **Read and Parse File**: - Read the target `.agent.md` or `.prompt.md` file - Extract frontmatter (description, mode, tools, model if specified) - Analyze body content to identify: - Task complexity (simple/moderate/complex/advanced) - Required reasoning depth (basic/intermediate/advanced/expert) - Code generation needs (minimal/moderate/extensive) - Multi-turn conversation requirements - Context window needs (small/medium/large) - Specialized capabilities (image analysis, long-context, real-time data) **Categorize Task Type**: Identify the primary task category based on content analysis: 1. **Simple Repetitive Tasks**: - Pattern: Formatting, simple refactoring, adding comments/docstrings, basic CRUD - Characteristics: Straightforward logic, minimal context, fast execution preferred - Keywords: format, comment, simple, basic, add docstring, rename, move 2. **Code Generation & Implementation**: - Pattern: Writing functions/classes, implementing features, API endpoints, tests - Characteristics: Moderate complexity, domain knowledge, idiomatic code - Keywords: implement, create, generate, write, build, scaffold 3. **Complex Refactoring & Architecture**: - Pattern: System design, architectural review, large-scale refactoring, performance optimization - Characteristics: Deep reasoning, multiple components, trade-off analysis - Keywords: architect, refactor, optimize, design, scale, review architecture 4. **Debugging & Problem-Solving**: - Pattern: Bug fixing, error analysis, systematic troubleshooting, root cause analysis - Characteristics: Step-by-step reasoning, debugging context, verification needs - Keywords: debug, fix, troubleshoot, diagnose, error, investigate 5. **Planning & Research**: - Pattern: Feature planning, research, documentation analysis, ADR creation - Characteristics: Read-only, context gathering, decision-making support - Keywords: plan, research, analyze, investigate, document, assess 6. **Code Review & Quality Analysis**: - Pattern: Security analysis, performance review, best practices validation, compliance checking - Characteristics: Critical thinking, pattern recognition, domain expertise - Keywords: review, analyze, security, performance, compliance, validate 7. **Specialized Domain Tasks**: - Pattern: Django/framework-specific, accessibility (WCAG), testing (TDD), API design - Characteristics: Deep domain knowledge, framework conventions, standards compliance - Keywords: django, accessibility, wcag, rest, api, testing, tdd 8. **Advanced Reasoning & Multi-Step Workflows**: - Pattern: Algorithmic optimization, complex data transformations, multi-phase workflows - Characteristics: Advanced reasoning, mathematical/algorithmic thinking, sequential logic - Keywords: algorithm, optimize, transform, sequential, reasoning, calculate **Extract Capability Requirements**: Based on `tools` in frontmatter and body instructions: - **Read-only tools** (search, fetch, usages, githubRepo): Lower complexity, faster models suitable - **Write operations** (edit/editFiles, new): Moderate complexity, accuracy important - **Execution tools** (runCommands, runTests, runTasks): Validation needs, iterative approach - **Advanced tools** (context7/*, sequential-thinking/*): Complex reasoning, premium models beneficial - **Multi-modal** (image analysis references): Requires vision-capable models ### 2. Model Evaluation Phase **Apply Model Selection Criteria**: For each available model, evaluate against these dimensions: #### Model Capabilities Matrix | Model | Multiplier | Speed | Code Quality | Reasoning | Context | Vision | Best For | | ----------------------- | ---------- | -------- | ------------ | --------- | ------- | ------ | ------------------------------------------------- | | GPT-4.1 | 0x | Fast | Good | Good | 128K | ✅ | Balanced general tasks, included in all plans | | GPT-5 mini | 0x | Fastest | Good | Basic | 128K | ❌ | Simple tasks, quick responses, cost-effective | | GPT-5 | 1x | Moderate | Excellent | Advanced | 128K | ✅ | Complex code, advanced reasoning, multi-turn chat | | GPT-5 Codex | 1x | Fast | Excellent | Good | 128K | ❌ | Code optimization, refactoring, algorithmic tasks | | Claude Sonnet 3.5 | 1x | Moderate | Excellent | Excellent | 200K | ✅ | Code generation, long context, balanced reasoning | | Claude Sonnet 4 | 1x | Moderate | Excellent | Advanced | 200K | ❌ | Complex code, robust reasoning, enterprise tasks | | Claude Sonnet 4.5 | 1x | Moderate | Excellent | Expert | 200K | ✅ | Advanced code, architecture, design patterns | | Claude Opus 4.1 | 10x | Slow | Outstanding | Expert | 1M | ✅ | Large codebases, architectural review, research | | Gemini 2.5 Pro | 1x | Moderate | Excellent | Advanced | 2M | ✅ | Very long context, multi-modal, real-time data | | Gemini 2.0 Flash (dep.) | 0.25x | Fastest | Good | Good | 1M | ❌ | Fast responses, cost-effective (deprecated) | | Grok Code Fast 1 | 0.25x | Fastest | Good | Basic | 128K | ❌ | Speed-critical simple tasks, preview (free) | | o3 (deprecated) | 1x | Slow | Good | Expert | 128K | ❌ | Advanced reasoning, algorithmic optimization | | o4-mini (deprecated) | 0.33x | Fast | Good | Good | 128K | ❌ | Reasoning at lower cost (deprecated) | #### Selection Decision Tree ``` START │ ├─ Task Complexity? │ ├─ Simple/Repetitive → GPT-5 mini, Grok Code Fast 1, GPT-4.1 │ ├─ Moderate → GPT-4.1, Claude Sonnet 4, GPT-5 │ └─ Complex/Advanced → Claude Sonnet 4.5, GPT-5, Gemini 2.5 Pro, Claude Opus 4.1 │ ├─ Reasoning Depth? │ ├─ Basic → GPT-5 mini, Grok Code Fast 1 │ ├─ Intermediate → GPT-4.1, Claude Sonnet 4 │ ├─ Advanced → GPT-5, Claude Sonnet 4.5 │ └─ Expert → Claude Opus 4.1, o3 (deprecated) │ ├─ Code-Specific? │ ├─ Yes → GPT-5 Codex, Claude Sonnet 4.5, GPT-5 │ └─ No → GPT-5, Claude Sonnet 4 │ ├─ Context Size? │ ├─ Small (<50K tokens) → Any model │ ├─ Medium (50-200K) → Claude models, GPT-5, Gemini │ ├─ Large (200K-1M) → Gemini 2.5 Pro, Claude Opus 4.1 │ └─ Very Large (>1M) → Gemini 2.5 Pro (2M), Claude Opus 4.1 (1M) │ ├─ Vision Required? │ ├─ Yes → GPT-4.1, GPT-5, Claude Sonnet 3.5/4.5, Gemini 2.5 Pro, Claude Opus 4.1 │ └─ No → All models │ ├─ Cost Sensitivity? (based on subscriptionTier) │ ├─ Free Tier → 0x models only: GPT-4.1, GPT-5 mini, Grok Code Fast 1 │ ├─ Pro (1000 premium/month) → Prioritize 0x, use 1x judiciously, avoid 10x │ └─ Pro+ (5000 premium/month) → 1x freely, 10x for critical tasks │ └─ Priority Factor? ├─ Speed → GPT-5 mini, Grok Code Fast 1, Gemini 2.0 Flash ├─ Cost → 0x models (GPT-4.1, GPT-5 mini) or lower multipliers (0.25x, 0.33x) ├─ Quality → Claude Sonnet 4.5, GPT-5, Claude Opus 4.1 └─ Balanced → GPT-4.1, Claude Sonnet 4, GPT-5 ``` ### 3. Recommendation Generation Phase **Primary Recommendation**: - Identify the single best model based on task analysis and decision tree - Provide specific rationale tied to file content characteristics - Explain multiplier cost implications for user's subscription tier **Alternative Recommendations**: - Suggest 1-2 alternative models with trade-off explanations - Include scenarios where alternatives might be preferred - Consider priority factor overrides (speed vs. quality vs. cost) **Auto-Selection Guidance**: - Assess if task is suitable for auto model selection (excludes premium models > 1x) - Explain when manual selection is beneficial vs. letting Copilot choose - Note any limitations of auto-selection for the specific task **Deprecation Warnings**: - Flag if file currently specifies a deprecated model (o3, o4-mini, Claude Sonnet 3.7, Gemini 2.0 Flash) - Provide migration path to recommended replacement - Include timeline for deprecation (e.g., "o3 deprecating 2025-10-23") **Subscription Tier Considerations**: - **Free Tier**: Recommend only 0x multiplier models (GPT-4.1, GPT-5 mini, Grok Code Fast 1) - **Pro Tier**: Balance between 0x (unlimited) and 1x (1000/month) models - **Pro+ Tier**: More freedom with 1x models (5000/month), justify 10x usage for exceptional cases ### 4. Integration Recommendations **Frontmatter Update Guidance**: If file does not specify a `model` field: ```markdown ## 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]] Rationale: [Explanation] ``` **Tool Alignment Check**: Verify model capabilities align with specified tools: - If tools include `context7/*` or `sequential-thinking/*`: Recommend advanced reasoning models (Claude Sonnet 4.5, GPT-5, Claude Opus 4.1) - If tools include vision-related references: Ensure model supports images (flag if GPT-5 Codex, Claude Sonnet 4, or mini models selected) - If tools are read-only (search, fetch): Suggest cost-effective models (GPT-5 mini, Grok Code Fast 1) ### 5. Context7 Integration for Up-to-Date Information **Leverage Context7 for Model Documentation**: When uncertainty exists about current model capabilities, use Context7 to fetch latest information: ```markdown **Verification with Context7**: Using `context7/get-library-docs` with library ID `/websites/github_en_copilot`: - Query topic: "model capabilities [specific capability question]" - Retrieve current model features, multipliers, deprecation status - Cross-reference against analyzed file requirements ``` **Example Context7 Usage**: ``` If unsure whether Claude Sonnet 4.5 supports image analysis: → Use context7 with topic "Claude Sonnet 4.5 vision image capabilities" → Confirm feature support before recommending for multi-modal tasks ``` ## Output Expectations ### Report Structure Generate a structured markdown report with the following sections: ```markdown # AI Model Recommendation Report **File Analyzed**: `[file path]` **File Type**: [chatmode | prompt] **Analysis Date**: [YYYY-MM-DD] **Subscription Tier**: [Free | Pro | Pro+] --- ## File Summary **Description**: [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**: - Strength 1: [specific to task] - Strength 2: [specific to task] - Strength 3: [specific to task] **Rationale**: [Detailed explanation connecting task characteristics to model capabilities] **Cost Impact** (for [Subscription Tier]): - Per request multiplier: [X]x - Estimated usage: [rough estimate based on task frequency] - [Additional cost context] ### 🔄 Alternative Options #### Option 1: [Model Name] - **Multiplier**: [X]x - **When to Use**: [specific scenarios] - **Trade-offs**: [compared to primary recommendation] #### Option 2: [Model Name] - **Multiplier**: [X]x - **When to Use**: [specific scenarios] - **Trade-offs**: [compared to primary recommendation] ### 📊 Model Comparison for This Task | Criterion | [Primary Model] | [Alternative 1] | [Alternative 2] | | ---------------- | --------------- | --------------- | --------------- | | Task Fit | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | | Code Quality | [rating] | [rating] | [rating] | | Reasoning | [rating] | [rating] | [rating] | | Speed | [rating] | [rating] | [rating] | | Cost Efficiency | [rating] | [rating] | [rating] | | Context Capacity | [capacity] | [capacity] | [capacity] | | Vision Support | [Yes/No] | [Yes/No] | [Yes/No] | ## 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 Code **To 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] ## Context7 Verification [If Context7 was used for verification] **Queries Executed**: - Topic: "[query topic]" - Library: `/websites/github_en_copilot` - Key Findings: [summary] ## 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. **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 ### Validation Steps - [ ] File successfully read and parsed - [ ] Frontmatter extracted correctly (or noted if missing) - [ ] Task complexity accurately categorized (Simple/Moderate/Complex/Advanced) - [ ] Primary task category identified from 8 options - [ ] Model recommendation aligns with decision tree logic - [ ] Multiplier cost explained for user's subscription tier - [ ] Alternative models provided with clear trade-off explanations - [ ] Auto-selection guidance included (recommended/not recommended/situational) - [ ] Deprecated model warnings included if applicable - [ ] Frontmatter update example provided (valid YAML) - [ ] Tool alignment verified (model capabilities match specified tools) - [ ] Context7 used when verification needed for latest model information - [ ] Report includes all required sections (summary, analysis, recommendation, implementation) ### Success Criteria - Recommendation is justified by specific file characteristics - Cost impact is clear and appropriate for subscription tier - Alternative models cover different priority factors (speed vs. quality vs. cost) - Frontmatter update is ready to copy-paste (no placeholders) - User can immediately act on recommendation (clear steps) - Report is readable and scannable (good structure, tables, emoji markers) ### Failure Triggers - File path is invalid or unreadable → Stop and request valid path - File is not `.agent.md` or `.prompt.md` → Stop and clarify file type - Cannot determine task complexity from content → Request more specific file or clarification - Model recommendation contradicts documented capabilities → Use Context7 to verify current info - Subscription tier is invalid (not Free/Pro/Pro+) → Default to Pro and note assumption ## Advanced Use Cases ### Analyzing Multiple Files If user provides multiple files: 1. Analyze each file individually 2. Generate separate recommendations per file 3. Provide summary table comparing recommendations 4. 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 only 2. Use side-by-side table format 3. Declare a winner with specific reasoning 4. Include cost comparison for subscription tier ### Migration Planning If file specifies a deprecated model: 1. Prioritize migration guidance in report 2. Test current behavior expectations vs. replacement model capabilities 3. Provide phased migration if breaking changes expected 4. Include rollback plan if needed ## 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 | Multiplier | Meaning | Free Tier | Pro Usage | Pro+ Usage | | ---------- | ------------------------------------------------ | --------- | --------- | ---------- | | 0x | Included in all plans, no premium count | ✅ | Unlimited | Unlimited | | 0.25x | 4 requests = 1 premium request | ❌ | 4000 uses | 20000 uses | | 0.33x | 3 requests = 1 premium request | ❌ | 3000 uses | 15000 uses | | 1x | 1 request = 1 premium request | ❌ | 1000 uses | 5000 uses | | 1.25x | 1 request = 1.25 premium requests | ❌ | 800 uses | 4000 uses | | 10x | 1 request = 10 premium requests (very expensive) | ❌ | 100 uses | 500 uses | ### Model Changelog & Deprecations (October 2025) **Deprecated Models** (Effective 2025-10-23): - ❌ o3 (1x) → Replace with GPT-5 or Claude Sonnet 4.5 for reasoning - ❌ o4-mini (0.33x) → Replace with GPT-5 mini (0x) for cost, GPT-5 (1x) for quality - ❌ Claude Sonnet 3.7 (1x) → Replace with Claude Sonnet 4 or 4.5 - ❌ Claude Sonnet 3.7 Thinking (1.25x) → Replace with Claude Sonnet 4.5 - ❌ Gemini 2.0 Flash (0.25x) → Replace with Grok Code Fast 1 (0.25x) or GPT-5 mini (0x) **Preview Models** (Subject to Change): - 🧪 Claude Sonnet 4.5 (1x) - Preview status, may have API changes - 🧪 Grok Code Fast 1 (0.25x) - Preview, free during preview period **Stable Production Models**: - ✅ GPT-4.1, GPT-5, GPT-5 mini, GPT-5 Codex (OpenAI) - ✅ Claude Sonnet 3.5, Claude Sonnet 4, Claude Opus 4.1 (Anthropic) - ✅ Gemini 2.5 Pro (Google) ### Auto Model Selection Behavior (Sept 2025+) **Included in Auto Selection**: - GPT-4.1 (0x) - GPT-5 mini (0x) - GPT-5 (1x) - Claude Sonnet 3.5 (1x) - Claude Sonnet 4.5 (1x) **Excluded from Auto Selection**: - Models with multiplier > 1 (Claude Opus 4.1, deprecated o3) - Models blocked by admin policies - Models unavailable in subscription plan (1x models in Free tier) **When Auto Selects**: - Copilot analyzes prompt complexity, context size, task type - Chooses from eligible pool based on availability and rate limits - Applies 10% multiplier discount on auto-selected models - Shows selected model on hover over response in Chat view ## Context7 Query Templates Use these query patterns when verification needed: **Model Capabilities**: ``` Topic: "[Model Name] code generation quality capabilities" Library: /websites/github_en_copilot ``` **Model Multipliers**: ``` Topic: "[Model Name] request multiplier cost billing" Library: /websites/github_en_copilot ``` **Deprecation Status**: ``` Topic: "deprecated models October 2025 timeline" Library: /websites/github_en_copilot ``` **Vision Support**: ``` Topic: "[Model Name] image vision multimodal support" Library: /websites/github_en_copilot ``` **Auto Selection**: ``` Topic: "auto model selection behavior eligible models" Library: /websites/github_en_copilot ``` --- **Last Updated**: 2025-10-28 **Model Data Current As Of**: October 2025 **Deprecation Deadline**: 2025-10-23 for o3, o4-mini, Claude Sonnet 3.7 variants, Gemini 2.0 Flash