external-model-selection

What this skill does

# External Model Selection

**Purpose**: Select the best external AI models for your specific task based on empirical performance data from production bug investigations.

**When Claude invokes this Skill**: When you need to consult external models, choose between different LLMs, or want diverse perspectives on architectural decisions, code bugs, or design choices.

---

## Quick Reference: Top Models

### 🥇 Tier 1 - Primary Recommendations (Use First)

**1. MiniMax M2** (`minimax/minimax-m2`)
- **Score**: 91/100 | **Speed**: 3 min ⚡⚡⚡ | **Cost**: $$
- **Best for**: Fast root cause analysis, production bugs, when you need simple implementable fixes
- **Proven**: Found exact bug (column calculation error) in 3 minutes during LSP investigation
- **Why it wins**: Pinpoint accuracy, avoids overengineering, focuses on simplest solution first

**2. Grok Code Fast** (`x-ai/grok-code-fast-1`)
- **Score**: 83/100 | **Speed**: 4 min ⚡⚡ | **Cost**: $$
- **Best for**: Debugging traces, validation strategies, test coverage design
- **Proven**: Step-by-step execution traces, identified tab/space edge cases
- **Why it wins**: Excellent debugging methodology, practical validation approach

**3. GPT-5.1 Codex** (`openai/gpt-5.1-codex`)
- **Score**: 80/100 | **Speed**: 5 min ⚡ | **Cost**: $$$
- **Best for**: Architectural redesign, long-term refactoring plans
- **Proven**: Proposed granular mapping system for future enhancements
- **Why it's valuable**: Strong architectural vision, excellent for planning major changes

**4. Sherlock Think Alpha** (`openrouter/sherlock-think-alpha`) 🎁 FREE
- **Score**: TBD | **Speed**: ~5 min ⚡ | **Cost**: FREE ($0!) 💰
- **Context**: 1.8M tokens (LARGEST context window available!)
- **Best for**: Massive codebase analysis, entire project reasoning, long-context planning
- **Secret**: Big player testing under weird name - don't let the name fool you
- **Specialties**:
  - Full codebase analysis (1.8M tokens = ~500k lines of code!)
  - Research synthesis across dozens of files
  - Protocol compliance & standards validation
  - Entire project architectural analysis
- **Why it's valuable**: FREE + massive context = ideal for comprehensive analysis
- **Use case**: When you need to analyze entire codebase or massive context (and it's FREE!)

**5. Gemini 3 Pro Preview** (`google/gemini-3-pro-preview`) ⭐ NEW
- **Score**: TBD | **Speed**: ~5 min ⚡ | **Cost**: $$$
- **Context**: 1M tokens (11.4B parameter model)
- **Best for**: Multimodal reasoning, agentic coding, complex architectural analysis, long-context planning
- **Strengths**: State-of-the-art on LMArena, GPQA Diamond, MathArena, SWE-Bench Verified
- **Specialties**:
  - Autonomous agents & coding assistants
  - Research synthesis & planning
  - High-context information processing (1M token window!)
  - Tool-calling & long-horizon planning
  - Multimodal analysis (text, code, images)
- **Why it's valuable**: Google's flagship frontier model, excels at inferring intent with minimal prompting
- **Use case**: When you need deep reasoning across massive context (entire codebase analysis)

### 🥈 Tier 2 - Specialized Use Cases

**6. Gemini 2.5 Flash** (`google/gemini-2.5-flash`)
- **Score**: 73/100 | **Speed**: 6 min ⚡ | **Cost**: $
- **Best for**: Ambiguous problems requiring exhaustive hypothesis exploration
- **Caution**: Can go too deep - best when truly uncertain about root cause
- **Value**: Low cost, thorough analysis when you need multiple angles

**7. GLM-4.6** (`z-ai/glm-4.6`)
- **Score**: 70/100 | **Speed**: 7 min 🐢 | **Cost**: $$
- **Best for**: Adding debug infrastructure, algorithm enhancements
- **Caution**: Tends to overengineer - verify complexity is warranted
- **Use case**: When you actually need priority systems or extensive logging

### ❌ AVOID - Known Reliability Issues

**Qwen3 Coder** (`qwen/qwen3-coder-30b-a3b-instruct`)
- **Score**: 0/100 | **Status**: FAILED (timeout after 8+ minutes)
- **Issue**: Reliability problems, availability issues
- **Recommendation**: DO NOT use for time-sensitive or production tasks

---

## Consultation Strategies

### Strategy 1: Fast Parallel Diagnosis (DEFAULT - 90% of use cases)

**Models**: `minimax/minimax-m2` + `x-ai/grok-code-fast-1`

```bash
# Launch 2 models in parallel (single message, multiple Task calls)
Task 1: golang-architect (PROXY MODE) → MiniMax M2
Task 2: golang-architect (PROXY MODE) → Grok Code Fast
```

**Time**: ~4 minutes total
**Success Rate**: 95%+
**Cost**: $$ (moderate)

**Use for**:
- Bug investigations
- Quick root cause diagnosis
- Production issues
- Most everyday tasks

**Benefits**:
- Fast diagnosis from MiniMax M2 (simplest solution)
- Validation strategy from Grok Code Fast (debugging trace)
- Redundancy if one model misses something

---

### Strategy 2: Comprehensive Analysis (Critical issues)

**Models**: `minimax/minimax-m2` + `openai/gpt-5.1-codex` + `x-ai/grok-code-fast-1`

```bash
# Launch 3 models in parallel
Task 1: golang-architect (PROXY MODE) → MiniMax M2
Task 2: golang-architect (PROXY MODE) → GPT-5.1 Codex
Task 3: golang-architect (PROXY MODE) → Grok Code Fast
```

**Time**: ~5 minutes total
**Success Rate**: 99%+
**Cost**: $$$ (high but justified)

**Use for**:
- Critical production bugs
- Architectural decisions
- High-impact changes
- When you need absolute certainty

**Benefits**:
- Quick fix (MiniMax M2)
- Long-term architectural plan (GPT-5.1)
- Validation and testing strategy (Grok)
- Triple redundancy

---

### Strategy 3: Deep Exploration (Ambiguous problems)

**Models**: `minimax/minimax-m2` + `google/gemini-2.5-flash` + `x-ai/grok-code-fast-1`

```bash
# Launch 3 models in parallel
Task 1: golang-architect (PROXY MODE) → MiniMax M2
Task 2: golang-architect (PROXY MODE) → Gemini 2.5 Flash
Task 3: golang-architect (PROXY MODE) → Grok Code Fast
```

**Time**: ~6 minutes total
**Success Rate**: 90%+
**Cost**: $$ (moderate)

**Use for**:
- Ambiguous bugs with unclear root cause
- Multi-faceted problems
- When initial investigation is inconclusive
- Complex system interactions

**Benefits**:
- Quick hypothesis (MiniMax M2)
- Exhaustive exploration (Gemini 2.5 Flash)
- Practical validation (Grok)
- Diverse analytical approaches

---

### Strategy 4: Full Codebase Analysis (Massive Context) 🆕

**Models**: `openrouter/sherlock-think-alpha` + `google/gemini-3-pro-preview`

```bash
# Launch 2 models in parallel
Task 1: golang-architect (PROXY MODE) → Sherlock Think Alpha
Task 2: golang-architect (PROXY MODE) → Gemini 3 Pro Preview
```

**Time**: ~5 minutes total
**Success Rate**: TBD (new strategy)
**Cost**: $$$ (one free, one paid = moderate overall)

**Use for**:
- Entire codebase architectural analysis
- Cross-file dependency analysis
- Large refactoring planning (50+ files)
- System-wide pattern detection
- Multi-module projects

**Benefits**:
- Sherlock: 1.8M token context (FREE!) - can analyze entire codebase
- Gemini 3 Pro: 1M token context + multimodal + SOTA reasoning
- Both have massive context windows for holistic analysis
- One free model reduces cost significantly

**Prompt Strategy**:
```markdown
Analyze the entire Dingo codebase focusing on [specific aspect].

Context provided:
- All files in pkg/ (50+ files)
- All tests in tests/ (60+ files)
- Documentation in ai-docs/
- Total: ~200k lines of code

Your task: [specific analysis goal]
```

---

### Strategy 5: Budget-Conscious (Cost-sensitive) 🎁

**Models**: `openrouter/sherlock-think-alpha` + `x-ai/grok-code-fast-1`

```bash
# Launch 2 models in parallel
Task 1: golang-architect (PROXY MODE) → Sherlock Think Alpha (FREE!)
Task 2: golang-architect (PROXY MODE) → Grok Code Fast
```

**Time**: ~5 minutes total
**Success Rate**: 85%+
**Cost**: $$ (Sherlock is FREE, only pay for Grok!)

**Use for**:
- **Cost-sensitive projects**
- **Large context needs on a budget**
- Non-critical investigations
- Exploratory analysis
- Learning and experimentation

**Benefits**:
- **Sherlock is completely FREE** with 1.8M context!
- *