rcode-memory-distill

Generate token-optimized Memory Bank distillates from source files.

2|1|Updated Apr 10, 2026

Search Tags:#markdown #context loading #token optimization #memory bank #distillation #llm workflow

Ready to run

npx skills add https://github.com/hanzlahabib/rcode --skill rcode-memory-distill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rcode-memory-distill
Source: https://github.com/hanzlahabib/rcode/tree/main/rcode/skills/core/rcode-memory-distill
Command: npx skills add https://github.com/hanzlahabib/rcode --skill rcode-memory-distill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It turns a large Memory Bank into a compact, fast-loading distillation so an AI can recover project context without burning tokens on repeated source reads.

Core Features & Use Cases

Regenerates two outputs: a full project distillate and a stack-only distillate.
Preserves every fact, decision, constraint, and relationship while stripping presentation overhead for token efficiency.
Uses source digests to skip work when files have not changed, making repeated runs safe and predictable.
Fits projects that need rapid context restoration from Memory Bank notes, people profiles, milestones, or incident records.

Quick Start

Ask the assistant to distill the Memory Bank and regenerate the project and stack distillates from the files under .rcode/memory.

Frequently Asked Questions about rcode-memory-distill

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

FAQPage Schema

How do I compress a memory bank for faster LLM context loading?▼

To compress a memory bank for faster LLM context loading, generate token-optimized distillates from source files. This process strips presentation overhead while preserving every fact, decision, constraint, and relationship to reduce token consumption during AI context recovery.

What is memory bank distillation and how does it preserve project context?▼

Memory bank distillation is a token optimization process that compacts large source files into fast-loading summaries. It preserves lossless facts and relationships while outputting project and stack distillates for rapid AI context restoration without repeated source reads.

How do I regenerate markdown distillates after memory bank changes?▼

To regenerate markdown distillates after memory bank changes, run the distillation process against the updated source files. It uses source digests for change detection, automatically skipping no-op tasks when files remain unchanged to ensure safe, predictable repeated runs.

Does token optimization for LLM workflows skip unchanged source files?▼

Yes, token optimization for LLM workflows uses digest-based change detection to skip unchanged source files. This no-op skip mechanism makes repeated distillation runs safe and predictable by avoiding unnecessary processing on unmodified memory bank entries.

Can I distill specific stack information separately from the full project context?▼

Yes, you can distill specific stack information separately from the full project context. The process generates two distinct outputs: a comprehensive project distillate and a stack-only distillate, both written to the distillates directory.