bmad-distillator

Generate lossless compressed document distillates for LLM workflows.

Updated Apr 25, 2026

Search Tags:#LLM optimization #information extraction #AI workflows #data distillation #document compression

Ready to run

npx skills add https://github.com/wahabmalik/whatsapp-chatbot --skill bmad-distillator-wahabmalik

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-distillator
Source: https://github.com/wahabmalik/whatsapp-chatbot/tree/main/.github/skills/bmad-distillator
Command: npx skills add https://github.com/wahabmalik/whatsapp-chatbot --skill bmad-distillator-wahabmalik

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of preserving the full content of source documents while optimizing them for consumption by Large Language Models (LLMs), providing a lossless, compressed version of the source material.

Core Features & Use Cases

Lossless Compression: Ensures that the compressed document retains all the information from the original, including facts, decisions, and relationships.
Optimized for LLMs: Designed to be efficient in terms of tokens, making it ideal for LLM workflows.
Use Case: When a user requests to 'distill documents' or 'create a distillate', this Skill can be activated to process a set of source documents and generate a distillate that can be used as context input for LLMs without information loss.

Quick Start

To generate a distillate from your source documents, activate the bmad-distillator skill and provide the paths to the source documents.

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 for LLM context without losing information?▼

You can distill documents by activating the Skill and providing paths to your source files. It analyzes and compresses the content into a structured output optimized for LLM workflows, ensuring no data is lost during the process.

What is data distillation for AI workflows and when do I need it?▼

Data distillation compresses source documents into LLM-optimized formats while retaining all facts, decisions, and relationships. It is needed when you must provide extensive context to LLMs without exceeding token limits or losing original information.

Do I need Python to run LLM-optimized document compression?▼

Yes, Python is required to execute the analysis, compression, and round-trip reconstruction scripts. These dependencies ensure the source documents are processed correctly and the resulting distillate is lossless.

Can I validate that my compressed distillate retains all original document information?▼

Validation is handled by a round-trip reconstruction script that verifies the compressed output matches the original source. This ensures the lossless compression retains all facts, decisions, and relationships from the source documents.

What are the limitations of using distillates for LLM context inputs?▼

Limitations include the requirement of a Python environment and specific scripts for analysis, compression, and validation. The Skill handles various input formats but relies on these dependencies to generate structured, lossless output for LLM workflows.