Convert complex documents into concise summaries for LLM processing.
npx skills add https://github.com/Imad-Oute/MicroHard --skill mh-distillator
Please help me install this Agent Skill. Skill: mh-distillator Source: https://github.com/Imad-Oute/MicroHard/tree/main/src/core/mh-distillator Command: npx skills add https://github.com/Imad-Oute/MicroHard --skill mh-distillator
The mh-distillator skill solves the problem of converting lengthy, complex documents into concise, token-efficient summaries that preserve essential information while being optimized for Large Language Model (LLM) consumption.
To distill the contents of 'project-report.pdf', simply use the mh-distillator command.
High-intent search queries and answers about installing and using this skill.
Document distillation converts lengthy, complex source documents into concise, token-efficient summaries optimized for LLM consumption. It extracts key facts, decisions, constraints, and relationships while applying lossless compression techniques to preserve essential information.
Lossless compression for document summarization is the process of extracting key facts, decisions, and relationships from complex documents and organizing them into efficient formats. This ensures all essential information is preserved without losing critical context for LLM processing.
Yes, you can distill multiple source documents into a single hyper-compressed distillate. The process extracts key facts, decisions, constraints, and relationships, offering either a single-file or semantically split output based on content complexity.
Yes, mh-distillator works with extensive project documentation and research reports. It hyper-compresses these complex documents into digestible formats, organizing extracted key information efficiently for quick reference by LLMs.
The best way to optimize lengthy documents for LLM context windows is applying lossless compression techniques to extract key facts and relationships. This organizes information into token-efficient, concise summaries suitable for LLM processing.
greghavensExtract text and metadata from PDF files using pdf-parse.
mistakeknotBenchmark token efficiency of agent workflows by analyzing prompt and model interactions.
Gull-StackExtract text and tables from PDFs using Python tools.
h4vzzCompress long chat histories and documents to fit token limits while preserving critical facts.
