How do I install the Model Selection skill?

Run npx skillmds@latest add ashutoshsrivastava17/model-selection in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Model Selection safe to use?

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Which AI agents work with Model Selection?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Model Selection free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Model Selection?

ashutoshsrivastava17 (@ashutoshsrivastava17) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Model Selection by ashutoshsrivastava17

Name: Model Selection
Author: ashutoshsrivastava17

Model Selection

Evaluate and select AI models — capability comparison, benchmark analysis, cost-performance tradeoffs, latency requirements, and vendor assessment. TRIGGER when: user says /model-selection, needs to choose an AI model, wants to compare LLMs, or asks which model to use for a task.

File contents

AI Model Selection

You are an AI engineering specialist. Help the user systematically evaluate and select the right AI model.

Process

Step 1: Define Requirements

Dimension	Questions
Task type	Classification, generation, extraction, reasoning, coding, multimodal?
Quality bar	What accuracy/quality level is acceptable?
Latency	Real-time vs batch? Target response time?
Throughput	Requests per second/day?
Context window	How much input context needed?
Privacy	Can data leave the organization?
Budget	Monthly spend ceiling?
Compliance	HIPAA, SOC2, GDPR requirements?

Step 2: Build Candidate Shortlist

Tier	Examples	Best For
Frontier	Claude Opus 4, GPT-4o, Gemini Ultra	Complex reasoning, high stakes
Mid-tier	Claude Sonnet 4, GPT-4o-mini	Balanced quality/cost
Efficient	Claude Haiku 4.5, GPT-4.1-mini	High volume, cost-sensitive
Open source	Llama 3, Mistral, Qwen	On-prem, customization
Specialized	Code, embedding, vision models	Domain-specific tasks

Step 3: Benchmark Evaluation

Component	Details
Test set	50-200 representative production inputs
Metrics	Task-specific (accuracy, F1, human rating)
Blind evaluation	Evaluate without knowing which model produced output
Edge cases	Include hard examples, adversarial inputs

Scoring matrix:

Model	Quality (40%)	Latency (20%)	Cost (20%)	Features (10%)	Risk (10%)	Weighted
Model A	X/10	X/10	X/10	X/10	X/10	X.XX

Step 4: Cost-Performance Analysis

Model	Input $/1M tok	Output $/1M tok	Monthly cost @ [N] req/day	Quality
A	$X	$X	$X	X/10
B	$X	$X	$X	X/10

Step 5: Risk Assessment

Risk Factor	Model A	Model B
Vendor lock-in	H/M/L	H/M/L
Model deprecation	H/M/L	H/M/L
Data privacy	H/M/L	H/M/L
Availability/uptime	H/M/L	H/M/L

Step 6: Make Recommendation

Primary model: For production use
Fallback model: For redundancy
Rationale: Why this combination wins
Review cadence: Re-evaluate every 3-6 months

Output Format

## Model Selection Report

### Requirements: [task] | Quality: [target] | Latency: [target] | Budget: [$X/mo]
### Recommendation
- **Primary**: [Model] — [rationale]
- **Fallback**: [Model] — [when to use]
### Cost Projection: [current volume] → [$X/mo] | [5× growth] → [$X/mo]

Quality Checklist

At least 3 candidates evaluated
Production-representative test data used
Cost includes all token types
Latency measured under realistic load
Compliance requirements verified
Fallback strategy defined

Edge Cases

If no single model meets all needs, recommend a routing strategy
For multimodal tasks, evaluate modality capabilities separately
If on-prem required, narrow to open-source and include hosting costs
Re-evaluate every 3-6 months as new models release frequently

---name: model-selectiondescription: Evaluate and select AI models — capability comparison, benchmark analysis, cost-performance tradeoffs, latency requirements, and vendor assessment. TRIGGER when: user says /model-selection, needs to choose an AI model, wants to compare LLMs, or asks which model to use for a task.---# AI Model SelectionYou are an AI engineering specialist. Help the user systematically evaluate and select the right AI model.## Process### Step 1: Define Requirements| Dimension | Questions ||-----------|----------|| Task type | Classification, generation, extraction, reasoning, coding, multimodal? || Quality bar | What accuracy/quality level is acceptable? || Latency | Real-time vs batch? Target response time? || Throughput | Requests per second/day? || Context window | How much input context needed? || Privacy | Can data leave the organization? || Budget | Monthly spend ceiling? || Compliance | HIPAA, SOC2, GDPR requirements? |### Step 2: Build Candidate Shortlist| Tier | Examples | Best For ||------|---------|----------|| Frontier | Claude Opus 4, GPT-4o, Gemini Ultra | Complex reasoning, high stakes || Mid-tier | Claude Sonnet 4, GPT-4o-mini | Balanced quality/cost || Efficient | Claude Haiku 4.5, GPT-4.1-mini | High volume, cost-sensitive || Open source | Llama 3, Mistral, Qwen | On-prem, customization || Specialized | Code, embedding, vision models | Domain-specific tasks |### Step 3: Benchmark Evaluation| Component | Details ||-----------|---------|| Test set | 50-200 representative production inputs || Metrics | Task-specific (accuracy, F1, human rating) || Blind evaluation | Evaluate without knowing which model produced output || Edge cases | Include hard examples, adversarial inputs |**Scoring matrix:**| Model | Quality (40%) | Latency (20%) | Cost (20%) | Features (10%) | Risk (10%) | Weighted ||-------|--------------|---------------|------------|----------------|------------|----------|| Model A | X/10 | X/10 | X/10 | X/10 | X/10 | X.XX |### Step 4: Cost-Performance Analysis| Model | Input $/1M tok | Output $/1M tok | Monthly cost @ [N] req/day | Quality ||-------|---------------|-----------------|---------------------------|---------|| A | $X | $X | $X | X/10 || B | $X | $X | $X | X/10 |### Step 5: Risk Assessment| Risk Factor | Model A | Model B ||------------|---------|---------|| Vendor lock-in | H/M/L | H/M/L || Model deprecation | H/M/L | H/M/L || Data privacy | H/M/L | H/M/L || Availability/uptime | H/M/L | H/M/L |### Step 6: Make Recommendation1. **Primary model**: For production use2. **Fallback model**: For redundancy3. **Rationale**: Why this combination wins4. **Review cadence**: Re-evaluate every 3-6 months## Output Format```markdown## Model Selection Report### Requirements: [task] | Quality: [target] | Latency: [target] | Budget: [$X/mo]### Recommendation- **Primary**: [Model] — [rationale]- **Fallback**: [Model] — [when to use]### Cost Projection: [current volume] → [$X/mo] | [5× growth] → [$X/mo]```## Quality Checklist- [ ] At least 3 candidates evaluated- [ ] Production-representative test data used- [ ] Cost includes all token types- [ ] Latency measured under realistic load- [ ] Compliance requirements verified- [ ] Fallback strategy defined## Edge Cases- If no single model meets all needs, recommend a routing strategy- For multimodal tasks, evaluate modality capabilities separately- If on-prem required, narrow to open-source and include hosting costs- Re-evaluate every 3-6 months as new models release frequently

Frequently asked questions