wiki-enrich

Convert daily journal entries into subject-based highlights and a monthly review draft.

5|1|Updated Apr 15, 2026
One-click install
npx skills add https://github.com/GYF0311/lorekit --skill wiki-enrich
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: wiki-enrich
Source: https://github.com/GYF0311/lorekit/tree/main/skills/wiki-enrich
Command: npx skills add https://github.com/GYF0311/lorekit --skill wiki-enrich

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lorekit enrich, lorekit search, wiki-fileback, lorekit lint.

What problem does it solve?

It helps you distill scattered daily journal entries into high-signal highlights and a structured monthly review, so your knowledge base evolves from “raw logs” to “usable insights” without cluttering or copying everything.

Core Features & Use Cases

  • Subject-based signal extraction: Groups journal items by subject (people, projects, concepts) to avoid dumping full conversations.
  • High-signal filtering: Keeps decisions, conclusions, new facts, lessons, and new resources while discarding emotional noise and duplicates.
  • Monthly review drafting with traceability: Produces a monthly recap draft that aggregates highlights and includes a pending approval list for fileback into沉淀层.
  • Safe workflow by design: Proposes changes only; actual writing is delegated to wiki-fileback, then followed by a quick lint check.

Quick Start

Ask your AI agent: “enrich my recent daily journal into a monthly review and propose the knowledge-base fileback, then run a quick lint check.”

Frequently Asked Questions about wiki-enrich

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

FAQPage Schema
How do I extract high-signal highlights from daily journal entries for a monthly review?

Subject-based signal extraction groups journal items by people, projects, or concepts to avoid dumping full conversations. It keeps decisions, conclusions, new facts, lessons, and new resources while discarding emotional noise and duplicates to produce high-signal highlights.

How to generate a monthly review draft from scattered notes with fileback proposals?

Generate a monthly review draft by scanning date ranges from daily journals, aggregating highlights, and producing an _月度复盘/YYYY-MM.md draft. This draft includes a pending approval list for knowledge-base fileback proposals with per-item journal sources before any write actions occur.

When do I need to enrich my daily notes into a structured knowledge base?

You need to enrich daily notes into a structured knowledge base during periodic maintenance such as month start or end, or when daily notes start to overwhelm you. This evolves raw logs into usable insights without cluttering or copying everything.

Does knowledge base enrichment automatically write files into the wiki?

Knowledge base enrichment does not automatically write files into the wiki. It proposes changes only by generating a draft for user approval, delegating actual writing to wiki-fileback, and following up with a quick lint check for safe workflow design.

What is the best way to maintain traceability when distilling daily logs into monthly insights?

The best way to maintain traceability when distilling daily logs is to generate fileback proposals with per-item journal sources. This ensures every highlight in your monthly review draft links back to its original daily entry.

What dependencies do I need to run wiki enrichment and lint checks on journal logs?

To run wiki enrichment and lint checks on journal logs, you need dependencies including lorekit enrich, lorekit search, wiki-fileback, and lorekit lint. These handle signal extraction, traceability, fileback proposals, and post-write validation respectively.