vault-distiller

Parse vault records and generate structured learning records with wikilinks.

228|28|Updated Feb 19, 2026
One-click install
npx skills add https://github.com/ssdavidai/alfred --skill vault-distiller
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vault-distiller
Source: https://github.com/ssdavidai/alfred/tree/main/src/alfred/_bundled/skills/vault-distiller
Command: npx skills add https://github.com/ssdavidai/alfred --skill vault-distiller

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vault records contain latent knowledge that often remains trapped in raw notes and artifacts. This skill parses operational records (sessions, conversations, notes, tasks, projects) and converts them into structured learning records with proper frontmatter, wikilinks, and file placement.

Core Features & Use Cases

  • Read operational vault records and distill learning artifacts (assumptions, decisions, constraints, contradictions, syntheses)
  • Link every learning record back to its source material with wikilinks and frontmatter for traceability
  • Prepare records for inclusion in the learnings base and enable cross-record synthesis

Quick Start

Run the vault distiller against a source vault record to produce a latent-learning learning record.

Frequently Asked Questions about vault-distiller

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

FAQPage Schema
How do I extract latent learnings from vault records?

To extract latent learnings from vault records, you parse operational notes and artifacts to identify assumptions, decisions, and constraints, then structure them into formal learning records. This skill automates that extraction by processing sessions, conversations, and tasks.

What is the best way to structure extracted learnings with frontmatter and wikilinks?

Structuring extracted learnings with frontmatter and wikilinks requires enforcing strict schemas during record creation. Using a vault CLI ensures the new learning record maintains traceability back to its source material through standardized metadata formatting.

How do I ensure traceability when distilling notes into a learning record?

To ensure traceability when distilling notes into a learning record, you must link every newly generated insight back to its original source material. This is achieved by embedding wikilinks and frontmatter that reference the specific conversations, tasks, or projects.

Do I need a vault CLI to automate learning extraction from notes?

You need a vault CLI to automate learning extraction if you want to enforce schemas, frontmatter, and wikilinks systematically. The CLI enforces the structural rules required to output valid learning records from raw operational data.

What types of vault records can be processed for knowledge distillation?

Vault records that can be processed for knowledge distillation include conversations, notes, tasks, sessions, and projects. Parsing these operational artifacts identifies latent knowledge like contradictions and syntheses to convert into structured formats.