memory-canonicalize

Convert raw memory items into wiki-ready markdown entries with category-specific structure.

Updated Jun 23, 2026
One-click install
npx skills add https://github.com/Walliiee/agent-harness --skill memory-canonicalize
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-canonicalize
Source: https://github.com/Walliiee/agent-harness/tree/main/skills/memory-canonicalize
Command: npx skills add https://github.com/Walliiee/agent-harness --skill memory-canonicalize

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns raw memory items into a consistent wiki entry shape so downstream writing and promotion steps stay aligned, avoid drift, and handle collisions or updates predictably.

Core Features & Use Cases

  • Slug normalization and collision checks: Converts proposed slugs to kebab-case, checks whether an entry already exists, and decides whether to create new or update existing content.
  • Category-specific wiki structure: Shapes content for agent behaviors, projects, concepts, tools, and people using the right section order and formatting.
  • Cross-link suggestions and index lines: Proposes related wiki links and generates a concise INDEX entry for publication.
  • Use Case: A memory promotion workflow passes in raw notes about a project, and this Skill returns a clean markdown entry plus the correct index line for the wiki writer to publish.

Quick Start

Ask the memory-canonicalize skill to shape these notes into a wiki-ready entry with an appropriate slug, category, related links, and index line.

Frequently Asked Questions about memory-canonicalize

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

FAQPage Schema
How do I convert raw memory notes into structured markdown wiki entries?▼

To convert raw memory notes into structured markdown wiki entries, the skill applies category-specific formatting to shape content for projects, concepts, or people while generating a normalized slug. This standardizes memory capture workflows into consistent wiki-ready markdown.

What is the best way to normalize slugs and detect collisions in a memory wiki?▼

The best way to normalize slugs and detect collisions is by converting proposed slugs to kebab-case and checking for existing entries. This process decides whether to safely update existing content or create a new markdown entry to prevent wiki drift.

How does cross-linking work when promoting memory items to a wiki?▼

Cross-linking works by proposing related wiki links based on existing candidate discovery during the memory promotion workflow. It identifies related entries and generates a concise index line alongside the formatted markdown content for publication.

Can I use memory canonicalization for updating existing wiki entries instead of creating new ones?▼

Yes, you can use memory canonicalization for updating existing wiki entries because it handles update-versus-create decisions. By checking for existing slugs, it determines whether to overwrite current content or generate a completely new markdown file.

When do I need to generate an index line for my markdown wiki?▼

You need to generate an index line for your markdown wiki when promoting memory items to ensure downstream writing steps stay aligned. It provides a concise publication reference that links the newly standardized entry into the broader wiki structure.