distill

Consolidate knowledge across memory systems into compact, structured candidates.

15|Updated Apr 9, 2026

Search Tags:#memory-systems #memory-management #confidence-scoring #ai-pipelines #tier-routing #knowledge-distillation

Ready to run

npx skills add https://github.com/komluk/scaffolding --skill distill-komluk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distill
Source: https://github.com/komluk/scaffolding/tree/main/skills/distill
Command: npx skills add https://github.com/komluk/scaffolding --skill distill-komluk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Distill knowledge from multiple sources into compact, structured memory for rapid retrieval and consistent decision-making across agents.

Core Features & Use Cases

Consolidates knowledge across memory systems into a compact, searchable set of candidates.
Applies confidence scoring and tier routing to determine which agent should access a given insight.
Supports frontmatter-driven metadata, modular components, and auditable updates.

Quick Start

Distill recent conversations into a structured knowledge index for fast retrieval.

Frequently Asked Questions about distill

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

FAQPage Schema

What is knowledge distillation for memory management in AI pipelines?▼

Knowledge distillation for AI pipelines consolidates multi-source data into compact, structured candidates for rapid retrieval. It applies confidence scoring and deterministic tier routing to ensure consistent cross-node inference and long-term memory access across agents.

How do I consolidate multi-source conversations into a structured memory index?▼

You consolidate multi-source conversations by distilling them into a searchable knowledge index. This process applies frontmatter-driven metadata and confidence scoring to automate tier routing, ensuring agents retrieve the correct insights rapidly.

How does confidence scoring work for tier routing across memory systems?▼

Confidence scoring for tier routing evaluates consolidated knowledge to determine which agent should access a specific insight. It uses deterministic routing rules and frontmatter-driven metadata to automate cross-node inference and manage long-term memory reliably.

Can I use frontmatter-driven metadata for auditable memory updates?▼

Yes, you can use frontmatter-driven metadata to maintain auditable updates across memory systems. It supports modular components and deterministic routing rules to track knowledge distillation and ensure structured, reliable long-term memory management.

What is the best way to automate cross-node inference for long-term memory?▼

The best way to automate cross-node inference is applying deterministic routing rules and confidence scoring during knowledge distillation. This approach structures multi-source data into a compact set of candidates for consistent decision-making across memory systems.

When should I not use deterministic routing rules for knowledge consolidation?▼

You should avoid deterministic routing rules for knowledge consolidation when multi-source data requires subjective interpretation rather than strict confidence scoring. Deterministic routing excels at structured memory management but lacks flexibility for ambiguous, unstructured cross-node inference.