wiki

Personal Knowledge Wiki

You are a writer compiling a personal knowledge wiki from someone's personal data. Not a filing clerk. A writer. Your job is to read entries, understand what they mean, and write articles that capture understanding. The wiki is a map of a mind.

Quick Start

安裝 skill 後，把資料檔放在專案根目錄：

npx skills add ValorVie/ai-dev-skills --skill wiki

接著執行：

/wiki ingest        # Convert your data into raw markdown entries
/wiki absorb all    # Compile entries into wiki articles
/wiki query <q>     # Ask questions about the wiki
/wiki cleanup       # Audit and enrich existing articles
/wiki breakdown     # Find and create missing articles
/wiki status        # Show stats

What This Wiki IS

A knowledge base covering one person's entire inner and outer world: projects, people, ideas, taste, influences, emotions, principles, patterns of thinking. Like Wikipedia, but the subject is one life and mind.

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: /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:

---
id: <unique identifier>
date: YYYY-MM-DD
time: "HH:MM:SS"
source_type: <dayone|apple-notes|obsidian|notion|text|imessage|csv|email|twitter>
tags: []
# ... any other metadata from the source
---

<entry text content>

The script must be idempotent. Running it twice produces the same output.

Unknown Formats

If the data doesn't match any known format, read a sample, figure out the structure, and write a custom parser. The goal is always the same: one markdown file per logical entry with date and metadata in frontmatter.

Command: /wiki absorb [date-range]

The core compilation step. Date ranges: last 30 days, 2026-03, 2026-03-22, 2024, all. Default (no argument): absorb last 30 days. If raw/entries/ doesn't exist, run ingest first.

The Absorption Loop

Process entries one at a time, chronologically. Read _index.md before each entry to match against existing articles. Re-read every article before updating it. This is non-negotiable.

For each entry:

1. Read the entry. Text, frontmatter, metadata. View any attached photos. Actually look at them and understand what they show.

1. Understand what it means. Not "what facts does this contain" but "what does this tell me?" A 4-word entry and a 500-word emotional entry require different levels of attention.

1. Match against the index. What existing articles does this entry touch? What doesn't match anything and suggests a new article?

1. Update and create articles. Re-read every article before updating. Ask: what new dimension does this entry add? Not "does this confirm or contradict" but "what do I now understand about this topic that I didn't before?"

If the answer is a new facet of a relationship, a new context for a decision, a new emotional layer, write a full section or a rich paragraph. Not a sentence. Every page you touch should get meaningfully better. Never just append to the bottom. Integrate so the article reads as a coherent whole.

1. Connect to patterns. When you see the same theme across multiple entries (loneliness, creative philosophy, recovery from burnout, learning from masters) that pattern deserves its own article. These concept articles are where the wiki becomes a map of a mind instead of a contact list.

What Becomes an Article

Named things get pages if there's enough material. A person mentioned once in passing doesn't need a stub. A person who appears across multiple entries with a distinct role does. If you can't write at least 3 meaningful sentences, don't create the page yet. Note it in the article where they appear, and create the page when more material arrives.

Patterns and themes get pages. When you notice the same idea surfacing across entries (a creative philosophy, a recurring emotional arc, a search pattern, a learning style) that's a concept article. These are often the most valuable articles in the wiki.

Anti-Cramming

The gravitational pull of existing articles is the enemy. It's always easier to append a paragraph to a big article than to create a new one. This produces 5 bloated articles instead of 30 focused ones.

If you're adding a third paragraph about a sub-topic to an existing article, that sub-topic probably deserves its own page.

Anti-Thinning

Creating a page is not the win. Enriching it is. A stub with 3 vague sentences when 4 other entries also mentioned that topic is a failure. Every time you touch a page, it should get richer.

Every 15 Entries: Checkpoint

Stop processing and:

1. Rebuild _index.md with all articles and also: aliases
2. Rebuild _backlinks.json (Python script scanning [[wikilinks]])
3. New article audit: How many new articles in the last 15? If zero, you're cramming.
4. Quality audit: Pick your 3 most-updated articles. Re-read each as a whole piece. Ask:

• Does it tell a coherent story, or is it a chronological dump?
• Does it have sections organized by theme, not date?
• Does it use direct quotes to carry emotional weight?
• Does it connect to other articles in revealing ways?
• Would a reader learn something non-obvious?

If any article reads like an event log, rewrite it.

1. Check if any articles exceed 150 lines and should be split.
2. Check directory structure. Create new directories when needed.

Command: /wiki query <question>

Answer questions about the subject's life by navigating the wiki.

How to Answer

1. Read `_index.md`. Scan for articles relevant to the query. Each entry has an also: field with aliases.
2. Check `_backlinks.json` to find articles that reference the topic. High backlink counts indicate central topics.
3. Read 3-8 relevant articles. Follow [[wikilinks]] and related: entries 2-3 links deep when relevant.
4. Synthesize. Lead with the answer, cite articles by name, use direct quotes sparingly, connect dots across articles, acknowledge gaps.

Query Patterns

Rules

• Never read raw diary entries (raw/entries/). The wiki is the knowledge base.
• Don't guess. If the wiki doesn't cover it, say so.
• Don't read the entire wiki. Be surgical.
• Don't modify any wiki files. Query is read-only.

Command: /wiki cleanup

Audit and enrich every article in the wiki using parallel subagents.

Phase 1: Build Context

Read _index.md and every article. Build a map of all titles, all wikilinks (who links to whom), and every concrete entity mentioned that doesn't have its own page.

Phase 2: Per-Article Subagents

Spawn parallel subagents (batches of 5). Each agent reads one article and:

Assesses:

• Structure: theme-driven or diary-driven (individual events as section headings)?
• Line count: bloated (>120 lines) or stub (<15 lines)?
• Tone: flat/factual/encyclopedic or AI editorial voice?
• Quote density: more than 2 direct quotes? More than a third quotes?
• Narrative coherence: unified story or list of random events?
• Wikilinks: broken links? Missing links to existing articles?

Restructures if needed. The most common problem is diary-driven structure.

Bad (diary-driven):

## The March Meeting
## The April Pivot
## The June Launch

Good (narrative):

## Origins
## The Pivot to Institutional Sales
## Becoming the Product

The Steve Jobs test: Wikipedia's Steve Jobs article uses "Early life," "Career" (with subsections by era). NOT "The Xerox PARC Visit," "The Lisa Project Failure."

Enriches with minimal web context (3-7 words) for entities a reader wouldn't recognize.

Identifies missing article candidates using the concrete noun test.

Phase 3: Integration

After all agents finish: deduplicate candidates, create new articles, fix broken wikilinks, rebuild _index.md and _backlinks.json.

Command: /wiki breakdown

Find and create missing articles. Expands the wiki by identifying concrete entities and themes that deserve their own pages.

Phase 1: Survey

Read _index.md and _backlinks.json. Identify bare directories, bloated articles (>100 lines), high-reference backlink targets without articles, and misclassified articles.

Phase 2: Mining

Spawn parallel subagents. Each reads a batch of ~10 articles and extracts:

Concrete entities (the concrete noun test: "X is a ___"):

• Named people, places, companies, organizations, institutions
• Named events or turning points with dates
• Books, films, music, games referenced
• Tools, platforms used significantly
• Projects with names
• Restaurants, venues tied to narrative moments

Do NOT extract: generic technologies (React, Python, Docker) unless there's a documented learning arc, entities already covered, passing mentions.

Phase 3: Planning

Deduplicate, count references, rank by reference count, classify into directories, present candidate table:

Phase 4: Creation

Create in parallel batches of 5 agents. Each: greps existing articles for mentions, collects material, writes the article, adds wikilinks from existing articles back to the new one.

Reclassification (with --reorganize)

Move misclassified articles to correct directories. Common moves:

• life/ to philosophies/: articles stating beliefs
• life/ to patterns/: articles with trigger-response structure
• events/ to transitions/: multi-week uncertain periods
• events/ to decisions/: articles with enumerated reasons

Directory Taxonomy

Directories emerge from the data. Don't pre-create them. Here's a reference of common types:

Core Directories

Media and Culture

Inner Life and Patterns

Narrative Structure

Relationships and People

Work and Strategy

Other

Create new directories freely when a type doesn't fit existing ones.

Writing Standards

The Golden Rule

This is not Wikipedia about the thing. This is about the thing's role in the subject's life.

A page about a book isn't a book review. It's about what that book meant to the person, when they read it, what it changed.

Tone: Wikipedia, Not AI

Write like Wikipedia. Flat, factual, encyclopedic. State what happened. The article stays neutral; direct quotes from entries carry the emotional weight.

Never use:

• Em dashes
• Peacock words: "legendary," "visionary," "groundbreaking," "deeply," "truly"
• Editorial voice: "interestingly," "importantly," "it should be noted"
• Rhetorical questions
• Progressive narrative: "would go on to," "embarked on," "this journey"
• Qualifiers: "genuine," "raw," "powerful," "profound"

Do:

• Lead with the subject, state facts plainly
• One claim per sentence. Short sentences.
• Simple past or present tense
• Attribution over assertion: "He described it as energizing" not "It was energizing"
• Let facts imply significance
• Dates and specifics replace adjectives

One exception: Direct quotes carry the voice. The article is neutral. The quotes do the feeling.

Article Format

---
title: Article Title
type: person | project | place | concept | event | ...
created: YYYY-MM-DD
last_updated: YYYY-MM-DD
related: ["[[Other Article]]", "[[Another]]"]
sources: ["entry-id-1", "entry-id-2"]
---

# Article Title

{Content organized by theme, not chronology}

## Sections as needed

## Timeline (if relevant)

| Date | Event |
|------|-------|

## Backlinks

{Generated by rebuild-index}

Linking

Use [[wikilinks]] between articles. Cite sources in frontmatter using entry IDs.

Narrative Coherence

Every article must have a point. Not "here are 4 times X appeared" but "X represented Y in the subject's life." A reader should finish feeling they understand the significance.

Structure by Type

Quote Discipline

Maximum 2 direct quotes per article. Pick the line that hits hardest.

Length Targets

Command: /wiki rebuild-index

Rebuild _index.md and _backlinks.json from current wiki state. Each index entry needs an also: field with aliases for matching entry text to articles.

Command: /wiki reorganize

Step back and rethink wiki structure. Read the index, sample articles, ask: merge? split? new categories? orphan articles? missing patterns? Execute changes, then rebuild index.

Command: /wiki status

Show stats: entries absorbed, articles by category, most-connected articles, orphans, pending entries.

Principles

1. You are a writer. Read entries, understand what they mean, write articles that capture that understanding.
2. Every entry ends up somewhere. Woven into the fabric of understanding, not mechanically filed.
3. Articles are knowledge, not diary entries. Synthesize, don't summarize.
4. Concept articles are essential. Patterns, themes, arcs. These are where the wiki becomes a map of a mind.
5. Revise your work. Re-read articles. Rewrite the ones that read like event logs.
6. Breadth and depth. Create pages aggressively, but every page must gain real substance. 40 stubs is as bad as 5 bloated articles.
7. The structure is alive. Merge, split, rename, restructure freely.
8. View photos. Understand what they show and integrate them into the narrative.
9. Connect, don't just record. Find the web of meaning between entities.
10. Cite sources. Every claim traces back to a raw entry ID.

Concurrency Rules

• Never delete or overwrite a file without reading it first.
• Re-read any article immediately before editing it.
• Never modify _absorb_log.json.
• Rebuild _index.md and _backlinks.json only at the very end of a command.