bmad-distillator

Convert source documents into lossless, token-efficient distillates for LLM consumption.

1|Updated Feb 25, 2026

Search Tags:#document processing #LLM optimization #AI integration #document distillation #information compression

Ready to run

npx skills add https://github.com/thavirak-svay/rentify-backend --skill bmad-distillator-thavirak-svay

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-distillator
Source: https://github.com/thavirak-svay/rentify-backend/tree/main/_bmad/core/skills/bmad-distillator
Command: npx skills add https://github.com/thavirak-svay/rentify-backend --skill bmad-distillator-thavirak-svay

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scripts.analyze_sources, agents.distillate-compressor, agents.round-trip-reconstructor, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The bmad-distillator Skill addresses the issue of creating dense, informative distillates from source documents without losing any critical information, making them ideal for consumption by Large Language Models (LLMs).

Core Features & Use Cases

Lossless Compression: Converts entire documents into highly compressed, token-efficient distillates while preserving all essential information.
Optimization for LLMs: Ensures that distillates are well-suited for processing by LLMs, enhancing their performance in downstream tasks.
Flexible Usage: Can handle multiple source documents, including those with various formats and structures.
Use Case: Utilize the bmad-distillator to compress a research report, a product brief, and its discovery notes into a single distillate for further analysis by an AI model.

Quick Start

Use the bmad-distillator to create a distillate of the 'product-brief.md' and its 'product-brief-discovery-notes.md' files.

Frequently Asked Questions about bmad-distillator

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

FAQPage Schema

How do I compress source documents into a token-efficient format for LLM consumption?▼

You can compress source documents into token-efficient distillates by applying lossless compression, which preserves all essential information while optimizing the text for LLM processing. This handles research, product development, and technical documentation scenarios.

What is lossless document distillation and when do I need it for AI integration?▼

Lossless document distillation converts entire documents into highly compressed, token-efficient formats without losing critical information. You need it when preparing research reports, product briefs, and discovery notes for efficient consumption by Large Language Models.

Can I combine multiple source documents with different formats into a single distillate?▼

Yes, you can process multiple source documents with various formats and structures into a single distillate. For example, you can combine a product brief and its discovery notes into one compressed output for further analysis by an AI model.

Do I need Python libraries to optimize documents for LLM token efficiency?▼

Yes, you need specific Python libraries for analysis and compression to create LLM-optimized distillates. These libraries process the text files and apply the lossless compression required to ensure token efficiency for AI model consumption.

What's the best way to prepare technical documentation for LLM processing without losing information?▼

The best way to prepare technical documentation without losing information is using lossless compression to create dense, informative distillates. This ensures all essential information is preserved while making the content highly token-efficient for LLM consumption.