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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmodel-recommendationExecute the skills CLI command in your project's root directory to begin installation:
Fetches model-recommendation from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate model-recommendation. Access via /model-recommendation in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
.agent.md or .prompt.md fileRequired:
${input:filePath:Path to .agent.md or .prompt.md file} - Absolute or workspace-relative path to the file to analyzeOptional:
${input:subscriptionTier:Pro} - User's Copilot subscription tier (Free, Pro, Pro+) - defaults to Pro${input:priorityFactor:Balanced} - Optimization priority (Speed, Cost, Quality, Balanced) - defaults to BalancedRead and Parse File:
.agent.md or .prompt.md fileCategorize Task Type:
Identify the primary task category based on content analysis:
Simple Repetitive Tasks:
Code Generation & Implementation:
Complex Refactoring & Architecture:
Debugging & Problem-Solving:
Planning & Research:
Code Review & Quality Analysis:
Specialized Domain Tasks:
Advanced Reasoning & Multi-Step Workflows:
Extract Capability Requirements:
Based on tools in frontmatter and body instructions:
Apply Model Selection Criteria:
For each available model, evaluate against these dimensions:
| 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) |
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
Primary Recommendation:
Alternative Recommendations:
Auto-Selection Guidance:
Deprecation Warnings:
Subscription Tier Considerations:
Frontmatter Update Guidance:
If file does not specify a model field:
## 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:
## 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:
context7/* or sequential-thinking/*: Recommend advanced reasoning models (Claude Sonnet 4.5, GPT-5, Claude Opus 4.1)Leverage Context7 for Model Documentation:
When uncertainty exists about current model capabilities, use Context7 to fetch latest information:
**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
Generate a structured markdown report with the following sections:
# 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 connectMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
github/awesome-copilot
github/awesome-copilot
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Registry listing for model-recommendation matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: model-recommendation is the kind of skill you can hand to a new teammate without a long onboarding doc.
model-recommendation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
model-recommendation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: model-recommendation is focused, and the summary matches what you get after install.
model-recommendation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
model-recommendation has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for model-recommendation matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: model-recommendation is focused, and the summary matches what you get after install.
model-recommendation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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