How does context caching reduce research costs?

By reusing document context in multi-pass workflows, Kimi provides an 83% discount on repeated input tokens, making deep, iterative analysis significantly cheaper.

Can I use this for large datasets?

Yes, the skill includes strategic decision trees for batching and sampling when dealing with datasets of 50 to 100+ documents.

What models does this skill support?

This skill is optimized specifically for Kimi K2.5, utilizing its unique pricing structure, multimodal capabilities, and agentic reasoning for qualitative work.

How do I estimate costs before starting a project?

You can run the provided estimation script or ask the @research-configurator agent to calculate costs based on your document count and planned analysis passes.

Qual Analysis Orchestrator

Name: Qual Analysis Orchestrator
Author: linxule

bylinxule

•

データサイエンスとML

Orchestrates cost-effective qualitative research workflows using Kimi K2.5 models and advanced context caching strategies.

This skill provides a comprehensive framework for planning and budgeting AI-assisted qualitative research within Claude Code. By leveraging the specific pricing advantages of Kimi K2.5, it helps researchers estimate API costs for coding projects, design efficient multi-pass analysis strategies, and maximize savings through context caching. Whether you are migrating from Claude or starting a new research project, this orchestrator ensures that interpretive depth remains the priority by removing financial uncertainty from large-scale document analysis and providing structured decision trees for batching and sampling.

主な機能

012 GitHub stars

02Strategic batching and sampling recommendations for large document sets

03Precise cost estimation for Kimi K2.5 qualitative coding projects

04Context caching modeling to achieve up to 83% input token savings

05Multi-pass workflow planning for deep interpretive and dialogical reasoning

06Comparative cost analysis for users migrating from Claude models

ユースケース

01Designing a two-pass coding workflow to balance speed and interpretive depth

02Estimating the budget for coding a large corpus of interview transcripts

03Comparing the ROI of switching from Claude to Kimi for qualitative research

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add linxule/interpretive-orchestration qual-analysis-orchestration

For use in Claude.ai and ChatGPT

Download Skill