| name | compile |
| description | Use when: building or updating wiki knowledge from markdown sources. Handles both quick compile (single source) and deep compile (cross-source synthesis). Do NOT use when: importing new material (use ingest) or health-checking (use lint). |
Compile
Transform markdown extractions into structured wiki knowledge.
Quick Compile(单源编译)
Triggered after ingest. Input: one markdown file from markdown/. Output: one summary in wiki/sources/.
Output Requirements
-
YAML frontmatter — must include:
title: article title
source: original URL or file path (from input frontmatter)
tags: 3-8 topic tags (lowercase, hyphenated)
date: ingest date (from input frontmatter)
-
Summary body — Obsidian-compatible markdown:
- Concise but comprehensive summary of the article
- Key takeaways as a bullet list
- Use
[[wikilinks]] to link related concepts (even if the target doesn't exist yet)
- Preserve source attribution — never fabricate claims not in the original
-
Language — match the language of the source material
Example Output
---
title: Attention Is All You Need
source: https://arxiv.org/abs/1706.03762
tags: [transformer, attention, deep-learning, nlp]
date: 2026-04-03
---
## Summary
The paper introduces the Transformer architecture...
## Key Takeaways
- Self-attention replaces recurrence and convolution
- Multi-head attention allows attending to different representation subspaces
- Positional encoding compensates for the lack of recurrence
## Related
- [[attention-mechanism]]
- [[sequence-to-sequence]]
Deep Compile(深度编译)
Cross-source synthesis. You have full write access to the wiki — create, update, and reorganize as needed.
What You Do
- Read
_index/master.md to understand current state
- Identify new/unprocessed sources — sources in
sources/ that haven't been synthesized into concepts yet (check wikilink references)
- Create or update
concepts/ articles — synthesize knowledge across multiple sources into standalone concept articles
- Create or update
maps/ articles — high-level overviews, timelines, comparison tables that connect multiple concepts
- Update
_index/ — keep master.md, by-topic.md, and other indexes current
- Update
SCHEMA.md — if the wiki's organizational structure has evolved, reflect it
Concepts (concepts/)
A concept article synthesizes knowledge about one topic from multiple sources.
---
title: Transformer Architecture
tags: [transformer, deep-learning, architecture]
sources: [attention-is-all-you-need, bert-paper, gpt-overview]
date: 2026-04-03
---
## Overview
Brief definition and significance.
## Key Ideas
- Idea 1 — synthesized from [[sources/attention-is-all-you-need]] and [[sources/bert-paper]]
- Idea 2 — ...
## Related Concepts
- [[self-attention]]
- [[positional-encoding]]
Rules:
- A concept must reference at least 2 sources — single-source knowledge stays in
sources/
- Always cite which sources support each claim
- Use
[[wikilinks]] liberally to connect concepts
Maps (maps/)
A map provides a bird's-eye view across multiple concepts.
Types:
- Topic overview — "Map of Deep Learning Architectures"
- Timeline — chronological progression of a field
- Comparison — structured comparison table across approaches
- Learning path — suggested reading order for a topic
---
title: Deep Learning Architectures Overview
type: topic-overview
tags: [deep-learning, architecture, map]
date: 2026-04-03
---
## Landscape
Brief overview of the field.
## Architectures
| Architecture | Key Innovation | Era | Sources |
|---|---|---|---|
| CNN | Spatial feature extraction | 2012 | [[sources/alexnet]] |
| Transformer | Self-attention | 2017 | [[sources/attention-is-all-you-need]] |
## Related Concepts
- [[concepts/transformer-architecture]]
- [[concepts/convolutional-networks]]
Rules:
- Maps synthesize across concepts, not directly from sources
- Always include links to the concepts and sources that inform the map
Incremental Strategy
- Only process sources that are new since last deep compile
- When updating existing concepts/maps, extend rather than rewrite — add new information, update dates
- If a concept has grown too large, consider splitting it
- If new sources reveal connections between existing concepts, create a new map
SCHEMA.md Maintenance
After making structural changes, update SCHEMA.md to reflect:
- New topic categories that have emerged
- Changes to the organizational hierarchy
- New conventions or patterns you've established