bmad-distillator

Extract lossless, token-efficient distillates from source documents.

Updated Mar 25, 2026

Search Tags:#workflow #verification #llm #compression #frontmatter #token-efficiency #distillate

Ready to run

npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill bmad-distillator-jingyiwng

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-distillator
Source: https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher/tree/main/_bmad/core/skills/bmad-distillator
Command: npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill bmad-distillator-jingyiwng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Distillator enables lossless compression of source documents into a single distillate (or semantically partitioned distillates) that downstream LLM workflows can consume without losing information, addressing the need to minimize token usage while preserving facts, decisions, and constraints.

Core Features & Use Cases

Lossless distillates: preserve all source information with maximal token efficiency.
Multi-stage workflow: analyze, compress, verify, and optionally round-trip validate distillates.
Flexible outputs: single distillate beside sources or a folder of sectioned distillates.

Quick Start

Provide one or more source_documents and optional flags to generate a distillate next to the primary source.

Frequently Asked Questions about bmad-distillator

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

FAQPage Schema

How do I compress LLM context tokens without losing source document information?▼

Lossless distillation compresses source documents into token-efficient distillates while preserving all facts, decisions, and constraints. This minimizes token usage for downstream LLM workflows without sacrificing information, utilizing a multi-stage workflow to analyze, compress, and verify outputs.

What is the best way to verify that distilled LLM context retains all original facts?▼

Round-trip validation verifies distilled LLM context by triggering end-to-end checks using the --validate option. This enforces frontmatter-driven metadata and required inputs to ensure the compressed distillate remains completely lossless compared to the original source documents.

Can I generate multiple sectioned distillates instead of a single compressed file?▼

Yes, flexible outputs allow generating either a single distillate beside the primary source or a folder of semantically partitioned, sectioned distillates. This organizes dense LLM context into manageable partitions while maintaining maximal token efficiency.

Does the distillation workflow support frontmatter-driven metadata for LLM inputs?▼

Yes, the distillation workflow enforces frontmatter-driven metadata and required inputs for LLM contexts. This ensures structured tracking of the lossless compression stages, from initial document analysis and compression to final verification and optional round-trip validation.

What are the limitations of using lossless distillation for dense LLM context?▼

Lossless distillation for dense LLM context requires providing one or more source documents to function. While it maximizes token efficiency, the multi-stage analysis, compression, and verification process may introduce processing overhead compared to simple truncation or basic summarization methods.