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analysis-orchestration

Configure AI model selection, API cost estimation, and batch processing for coding workflows.

14|1|Updated Dec 8, 2025
Search Tags:#claude#batch processing#api management#research planning#ai models#cost estimation#qualitative coding
One-click install
npx skills add https://github.com/linxule/interpretive-orchestration --skill analysis-orchestration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analysis-orchestration
Source: https://github.com/linxule/interpretive-orchestration/tree/main/skills/analysis-orchestration
Command: npx skills add https://github.com/linxule/interpretive-orchestration --skill analysis-orchestration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill guides qualitative researchers in configuring their AI-assisted analysis approach, helping them choose the optimal AI model (e.g., Claude Opus, Sonnet, Haiku) for specific coding tasks, estimate API costs, and plan efficient batch processing strategies. It ensures cost-effective and high-quality AI collaboration.

Core Features & Use Cases

  • Model Selection Guidance: Recommends the best Claude model based on coding complexity, budget, and corpus size.
  • Cost Estimation: Provides rough API cost estimates for processing documents, considering length, passes, and model choice.
  • Batch Processing Strategy: Advises on how to group and process documents (e.g., individual, small batches, large batches) for different corpus sizes.
  • Use Case: Before starting Stage 2 coding, use this skill to decide if Claude Sonnet is sufficient for initial categorization or if Opus is needed for deep interpretive coding, and estimate the budget impact.

Quick Start

Estimate the cost for coding 25 documents, each averaging 5 pages, using the Sonnet model for 2 passes: node skills/analysis-orchestration/scripts/estimate-costs.js --documents 25 --avg-pages 5 --model sonnet --passes 2

Frequently Asked Questions about analysis-orchestration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate API costs for AI-assisted coding with Claude models?▼

Cost estimation calculates API expenses based on document count, page length, model selection (Opus, Sonnet, Haiku), and number of coding passes. Use the cost estimator script with your corpus parameters to project spending before processing begins.

Which Claude model should I choose for qualitative coding tasks?▼

Model selection depends on coding complexity, budget constraints, and corpus size. Sonnet suits initial categorization on modest budgets; Opus handles deep interpretive coding requiring nuanced reasoning across large document sets.

How do I plan batch processing for multiple documents in AI analysis workflows?▼

Batch processing strategy determines optimal grouping—individual, small batches, or large batches—based on corpus size and API throughput needs. Configuration ensures efficient document handling and cost-effective API usage across coding stages.

Can I use batch processing to reduce costs when coding large document collections?▼

Yes. Batch processing organizes documents into groups sized for your corpus, reducing per-document overhead and API management complexity. Strategic batching lowers total costs while maintaining code quality across multiple passes.

What information do I need before configuring my AI-assisted analysis workflow?▼

Prepare document count, average page length, target model (Opus/Sonnet/Haiku), and planned coding passes. These inputs drive model selection guidance, cost projections, and batch processing recommendations for your analysis pipeline.

Does this work with large-scale research projects involving hundreds of documents?▼

Yes. The Skill scales from small pilot studies to large corpora by recommending appropriate batch sizes and cost-efficient model strategies. Cost estimation and batching configuration adapt to corpus size for transparent budget planning at any scale.

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