name: analysis-orchestration
description: "This skill should be used when users ask about which AI model to use for coding, mentions 'cost', 'batch', 'API', 'configure analysis', wants to process multiple documents, or needs to understand model capabilities and costs for Stage 2."

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)

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)

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.

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