bmad-distillator

Compress source documents into lossless distillates with token estimates.

Updated Mar 24, 2026
One-click install
npx skills add https://github.com/imonmi/INTER-EDU --skill bmad-distillator-imonmi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bmad-distillator
Source: https://github.com/imonmi/INTER-EDU/tree/main/_bmad/core/skills/bmad-distillator
Command: npx skills add https://github.com/imonmi/INTER-EDU --skill bmad-distillator-imonmi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Lossless distillation of source documents into a single dense artifact suitable for downstream LLM workflows, preserving all factual content while removing human-centric overhead.

Core Features & Use Cases

  • Produce a single distillate or semantically partitioned distillates that preserve every fact, decision, constraint, and relationship from the sources.
  • Supports an end-to-end activation: analyze inputs, compress groups, verify completeness, and optionally run a round-trip validation for high-stakes content.
  • Outputs are portable and token-estimated, enabling downstream workflows like PRD creation, architecture design, or long-form content synthesis.

Quick Start

Provide one or more source documents, optionally specify downstream_consumer and token_budget, and run the tool to generate a distillate adjacent 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 documents for LLM context without losing factual information?▼

Lossless distillation compresses source documents by removing human-centric overhead while preserving every fact, decision, and relationship. This produces a dense, LLM-ready artifact with token estimates for downstream workflows like PRD creation or architecture design.

What is the best way to package large documents into a single context for LLM workflows?▼

Packaging large documents into a single context involves analyzing inputs, compressing groups, and verifying completeness. You can generate a portable distillate or semantically partitioned parts, optionally specifying a downstream consumer and token budget to fit your pipeline.

How do I split long documents semantically for downstream LLM pipelines?▼

Semantic splitting for downstream LLM pipelines is achieved through fan-out routing, which partitions a distillate into semantically coherent parts. This preserves all factual content and constraints while fitting specified token budgets for tasks like long-form content synthesis.

Can I validate that document compression preserved all original facts for high-stakes content?▼

Yes, you can validate document compression by running an optional round-trip validation step. This high-stakes verification follows the analyze, compress, and verify activation steps to ensure the distillate is completely lossless.

Does lossless document distillation require any external dependencies or libraries?▼

No external dependencies are required to perform lossless document distillation. The tool operates self-contained using its internal scripts to analyze inputs, compress groups, verify completeness, and output portable distillates with token estimates.

When should I avoid using lossless distillation for information extraction?▼

You should avoid lossless distillation if your downstream LLM workflow requires preserving the original document's narrative structure or human-centric formatting, as the process specifically strips human overhead to produce a dense, factual artifact.