Gen Wiki

Every entry must be absorbed somewhere. Nothing gets dropped. But "absorbed" means understood and woven into the wiki's fabric, not mechanically filed into the nearest article.

The question is never "where do I put this fact?" It is: "what does this mean, and how does it connect to what I already know?"

Directory Structure

your-project/
  data/                  # Your source files (DO NOT MODIFY after ingest)
  raw/entries/            # One .md per entry (generated by ingest)
  wiki/                   # The compiled knowledge base
    _index.md             # Master index with aliases
    _backlinks.json       # Reverse link index
    _absorb_log.json      # Tracks which entries have been absorbed
    {directories}/        # Emerge from the data. Don't pre-create.

You can override the wiki directory with --dir <name> on any command. Each version is its own universe. Never read from or reference another version.

Command: /gen-wiki ingest

Convert source data into individual .md files in raw/entries/. Write a Python script ingest.py to do this. This step is mechanical, no LLM intelligence needed.

Supported Data Formats

The ingest script should auto-detect the format. Here's how to handle common ones:

Day One JSON (*.json with entries array): Each entry becomes a file. Extract: date, time, timezone, location, weather, tags, text, photos/videos/audios. Map dayone-moment:// URLs to relative file paths.

Apple Notes (exported .html, .txt, or .md files): Each note becomes a file. Extract: title (first line or filename), creation date (from metadata or filename), folder/tag, body text. Strip HTML formatting if needed.

Obsidian Vault (folder of .md files): Each note becomes a file. Preserve frontmatter. Extract: title, date (from frontmatter or filename), tags, body. Convert [[wikilinks]] to plain text for raw entries.

Notion Export (.md or .csv files): Each page becomes a file. Extract: title, properties/metadata, body. Handle nested pages by flattening with parent context.

Plain Text / Markdown Files (folder of .txt or .md): Each file becomes an entry. Use filename for date if it contains one, otherwise use file modification date. First line or filename becomes the title.

iMessage Export (.csv or chat logs): Group by conversation and date. Each day's conversation with one person becomes an entry. Extract: date, participants, messages.

CSV / Spreadsheet (.csv, .tsv): Each row becomes an entry. Use column headers as frontmatter fields. Identify the date column and text/content column automatically.

Email Export (.mbox, .eml): Each email becomes an entry. Extract: date, from, to, subject, body. Strip signatures and quoted replies.

Twitter/X Archive (tweet.js or archive export): Each tweet becomes an entry. Extract: date, text, media URLs, reply context, engagement stats.

Output Format

Each file: {date}_{id}.md with YAML frontmatter:

---