wiki-query

wiki-query: Query the Wiki

The wiki has already done the synthesis work. Read strategically, answer precisely, and file good answers back so the knowledge compounds.

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Query Modes

Three depths. Choose based on the question complexity.

Mode	Trigger	Reads	Token cost	Best for
Quick	query quick: ... or simple factual Q	hot.md + index.md only	~1,500	"What is X?", date lookups, quick facts
Standard	default (no flag)	hot.md + index + 3-5 pages	~3,000	Most questions
Deep	query deep: ... or "thorough", "comprehensive"	Full wiki + optional web	~8,000+	"Compare A vs B across everything", synthesis, gap analysis

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Quick Mode

Use when the answer is likely in the hot cache or index summary.

1. Read wiki/hot.md. If it answers the question, respond immediately.
2. If not, read wiki/index.md. Scan descriptions for the answer.
3. If found in index summary, respond and do not open any pages.
4. If not found, say "Not in quick cache. Run as standard query?"

Do not open individual wiki pages in quick mode.

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Standard Query Workflow

1. Read wiki/hot.md first. It may already have the answer or directly relevant context.
2. Read wiki/index.md to find the most relevant pages (scan for titles and descriptions).
3. Read those pages. Follow wikilinks to depth-2 for key entities. No deeper.
4. Synthesize the answer in chat. Cite sources with wikilinks: (Source: [[Page Name]]).
5. Offer to file the answer: "This analysis seems worth keeping. Should I save it as wiki/questions/answer-name.md?"
6. If the question reveals a gap: say "I don't have enough on X. Want to find a source?"

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Deep Mode

Use for synthesis questions, comparisons, or "tell me everything about X."

1. Read wiki/hot.md and wiki/index.md.
2. Identify all relevant sections (concepts, entities, sources, comparisons).
3. Read every relevant page. No skipping.
4. If wiki coverage is thin, offer to supplement with web search.
5. Synthesize a comprehensive answer with full citations.
6. Always file the result back as a wiki page. Deep answers are too valuable to lose.

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Token Discipline

Read the minimum needed:

Start with	Cost (approx)	When to stop
hot.md	~500 tokens	If it has the answer
index.md	~1000 tokens	If you can identify 3-5 relevant pages
3-5 wiki pages	~300 tokens each	Usually sufficient
10+ wiki pages	expensive	Only for synthesis across the entire wiki

If hot.md has the answer, respond without reading further.

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Index Format Reference

The master index (wiki/index.md) looks like:

## Domains
- [[Domain Name]]: description (N sources)

## Entities
- [[Entity Name]]: role (first: [[Source]])

## Concepts
- [[Concept Name]]: definition (status: developing)

## Sources
- [[Source Title]]: author, date, type

## Questions
- [[Question Title]]: answer summary

Scan the section headers first to determine which sections to read.

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Domain Sub-Index Format

Each domain folder has a _index.md for focused lookups:

---
type: meta
title: "Entities Index"
updated: YYYY-MM-DD
---
# Entities

## People
- [[Person Name]]: role, org

## Organizations
- [[Org Name]]: what they do

## Products
- [[Product Name]]: category

Use sub-indexes when the question is scoped to one domain. Avoid reading the full master index for narrow queries.

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Filing Answers Back

Good answers compound into the wiki. Don't let insights disappear into chat history.

When filing an answer:

---
type: question
title: "Short descriptive title"
question: "The exact query as asked."
answer_quality: solid
created: YYYY-MM-DD
updated: YYYY-MM-DD
tags: [question, <domain>]
related:
  - "[[Page referenced in answer]]"
sources:
  - "[[wiki/sources/relevant-source.md]]"
status: developing
---

Then write the answer as the page body. Include citations. Link every mentioned concept or entity.

After filing, add an entry to wiki/index.md under Questions and append to wiki/log.md.

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Gap Handling

If the question cannot be answered from the wiki:

1. Say clearly: "I don't have enough in the wiki to answer this well."
2. Identify the specific gap: "I have nothing on [subtopic]."
3. Suggest: "Want to find a source on this? I can help you search or process one."
4. Do not fabricate. Do not answer from training data if the question is about the specific domain in this wiki.