analysis-orchestration

What this skill does

# analysis-orchestration

Model selection, cost estimation, and batch processing setup for AI-assisted coding. Helps researchers configure their analysis approach with awareness of tradeoffs.

## When to Use

Use this skill when:
- User asks about which AI model to use for coding
- User mentions "cost", "batch", "API", or "configure analysis"
- User wants to process multiple documents
- User needs to understand model capabilities and costs
- Starting Stage 2 and needing to plan the approach

## Capabilities

1. **Model Selection Guidance** - Help choose between models based on task needs
2. **Cost Estimation** - Estimate API costs before processing
3. **Batch Strategy** - Plan efficient document processing
4. **API Configuration** - Set up for programmatic coding (future)

## Model Selection Guide

### For Document Coding (Stage 2)

| Model | Best For | Cost | Quality |
|-------|----------|------|---------|
| Claude Opus 4.5 | Complex interpretive coding, nuanced themes | $$$ | Highest |
| Claude Sonnet 4 | Balanced quality and cost for systematic coding | $$ | High |
| Claude Haiku | Initial passes, high volume, simple categorization | $ | Good |

### Recommendation by Task

**Deep Interpretive Coding (@dialogical-coder)**
- Use: Opus 4.5 or Sonnet 4
- Why: Requires nuanced understanding, theoretical sensitivity
- Pattern: Process 5-10 documents per session with reflection

**Initial Categorization**
- Use: Sonnet 4 or Haiku
- Why: Applying established codes is less interpretively demanding
- Pattern: Batch process with human review

**Pattern Characterization**
- Use: Opus 4.5
- Why: Requires integration across documents, theoretical abstraction

## Cost Estimation

### Rough Estimates (2025 pricing)

| Documents | Model | Estimated Cost |
|-----------|-------|----------------|
| 10 interviews (~50 pages) | Opus | $15-25 |
| 10 interviews (~50 pages) | Sonnet | $5-10 |
| 50 documents | Opus | $75-125 |
| 50 documents | Sonnet | $25-50 |

**Variables:**
- Document length (tokens)
- Coding depth (passes per document)
- Output verbosity (full reasoning vs brief)

### Scripts

#### estimate-costs.js
Estimates API costs based on document characteristics.

```bash
node skills/analysis-orchestration/scripts/estimate-costs.js \
  --documents 25 \
  --avg-pages 5 \
  --model sonnet \
  --passes 2
```

**Returns:** Estimated cost range and token counts.

## Batch Processing Strategy

### Small Corpus (10-30 documents)
- Process individually with full dialogical coding
- High engagement, rich reasoning
- Best for: Interpretive research, theory building

### Medium Corpus (30-100 documents)
- Batch in groups of 10
- First pass: categorization
- Second pass: deep coding on interesting cases
- Best for: Mixed-methods, systematic reviews

### Large Corpus (100+ documents)
- Strategic sampling for deep coding
- Batch categorization with random quality checks
- Best for: Large-scale studies, triangulation

## Decision Trees

### Which Model Should I Use?

```
Is this initial exploratory coding?
├── Yes → Consider Haiku for volume, validate with Sonnet
└── No, this is interpretive coding
    ├── Budget constrained?
    │   ├── Yes → Sonnet 4 (good balance)
    │   └── No → Opus 4.5 (best quality)
    └── Need to process >50 documents?
        ├── Yes → Two-pass: Haiku then Sonnet on subset
        └── No → Single-pass with Sonnet or Opus
```

### How Many Documents Per Session?

```
Using @dialogical-coder (4-stage process)?
├── Yes → 5-10 documents per session (reflection breaks)
└── No, systematic application?
    ├── Complex coding scheme → 10-15 documents
    └── Simple categorization → 20-30 documents
```

## Integration with Interpretive Orchestration

### Stage 1
- No AI models needed (manual coding)
- Focus on human theoretical sensitivity

### Stage 2 Phase 1 (Parallel Streams)
- Stream A (theoretical): Sonnet for literature analysis
- Stream B (empirical): Start with Sonnet, elevate complex cases to Opus

### Stage 2 Phase 2 (Synthesis)
- Opus recommended for integration work
- Cross-stream synthesis requires nuanced reasoning

### Stage 2 Phase 3 (Pattern Characterization)
- Opus for pattern identification
- Sonnet for validation passes

## Related

- **Commands:** `/qual-configure-analysis` triggers this skill
- **Agents:** @research-configurator provides interactive guidance
- **Skills:** `coding-workflow/` for batch processing execution