# Interpretable Context Methodology (ICM)

> Folder structure as agent architecture for multi-stage AI workflows

Interpretable Context Methodology (formerly Model Workspace Protocol) replaces complex multi-agent orchestration frameworks with a structured filesystem of Markdown files. By organizing workflows into numbered stage directories, a single AI agent loads only the necessary context per stage, keeping prompt overhead low. Every intermediate output remains a plain text file, allowing users to easily inspect, audit, and edit outputs between steps.

Website: https://github.com/RinDig/Model-Workspace-Protocol-MWP-

## What it is for

- Convert raw documents into presentation slide decks via automated pipelines
- Turn content ideas and research notes into scripts and Remotion animation code
- Generate voice-driven explainer videos by coordinating narration generation, beat transcription, and video rendering
- Build custom multi-stage production systems for specialized business workflows without writing framework orchestration code

## Features

- Five-layer context hierarchy to prevent prompt bloat and keep token usage low
- Numbered stage folders that establish clear execution order and separation of concerns
- Markdown stage contracts defining explicit inputs, processing steps, and output files
- Human-in-the-loop edit surfaces where outputs of one stage can be manually reviewed before the next runs
- Integrated checkpoints and self-audit quality gates for creative decisions
- Config-once factory setup for brand voice, styling rules, and reusable domain skills

## Pricing

Open source. Free and open source (MIT license)

## Details

- Type: GitHub repo
- Category: [AI](https://xiry.app/topics/ai.md)
- Open source: yes
- Platforms: Command line, other
- For: Developers and AI practitioners building repeatable, human-supervised generative pipelines with Claude Code or similar agentic tools
- Alternative to: CrewAI, LangChain, AutoGen
- Tags: agents, claude, context engineering, markdown, orchestration, prompt engineering, workflow

## Why it was saved

- [@lostandlucky, 23 February 2026](https://www.instagram.com/reel/DVFbK9HDAar/): Connects and automates complex multi-step generative tasks (like generating course content, slides, and animations) into a repeatable pipeline.

## Compare

- [Alternatives to LangChain](https://xiry.app/alternatives/langchain.md)
- [Alternatives to CrewAI](https://xiry.app/alternatives/crewai.md)
- [Alternatives to AutoGen](https://xiry.app/alternatives/autogen.md)

## Related

- [Claude Opus Multi-Agent Orchestration Demo (opus.py)](https://xiry.app/resources/claude-opus-multi-agent-orchestration-demo-opus-py): A Python multi-agent pattern pairing Opus orchestration with Haiku worker instances. (Framework, Open source)
- [AIToolHub](https://xiry.app/resources/aitoolhub): Directory and consulting hub for AI tools, skills, and automations (Web app, Freemium)
- [Anthropic Console Prompt Generator](https://xiry.app/resources/anthropic-console-prompt-generator): Turn rough task descriptions into structured, production-ready AI prompts automatically (Web app, Freemium)
- [AutoAgent Optimization Journey Chart](https://xiry.app/resources/autoagent-optimization-journey-chart): Step chart tracking benchmark gains across autonomous harness-tuning experiments (Framework, Free)
- [Building Effective Agents: Workflow and Agent Patterns](https://xiry.app/resources/building-effective-agents-workflow-and-agent-patterns): Architectural framework categorizing LLM workflows and autonomous agent patterns (Framework, Free)
- [Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned](https://xiry.app/resources/building-effective-ai-coding-agents-for-the-terminal-scaffol): A research paper formalizing architecture and context engineering for CLI coding agents. (Article or course, Free)

Updated 3 October 2026. Page: https://xiry.app/resources/interpretable-context-methodology-icm

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