Wiki Read

Ask the wiki a question and get a cited answer. If the wiki doesn't have the answer, automatically researches using whatever tools are available, ingests the results, and answers from the new pages.

Resolve .wiki/ from plugin install scope. If not found, say "No wiki found. Use /wiki-write to create one."

Arguments

• /wiki-read <question> — standard: search wiki first, research if not found, answer with citations
• /wiki-read quick <question> — index scan only, no research fallback (fastest)
• /wiki-read deep <question> — full wiki search + raw sources + automatic multi-channel research if needed (most thorough)

Process

Launch the wiki-reader agent with the question and depth level.

Standard depth (default)

1. Read .wiki/index.md, identify 2-4 relevant pages
2. Use bin/search-fulltext.py for ranked results
3. Read the relevant pages, synthesize a cited answer with [[slug]] references
4. If NOT found or insufficient: automatically research using whatever tools are available:
   • Discover available tools at runtime (WebSearch, WebFetch, wiki_wikipedia_search for factual/encyclopedic topics, any MCP tools like Perplexity, Context7, etc.)
   • Search using available tools, fetch and extract content
   • Ingest results via wiki-writer agent (mode: ingest)
   • Answer from the newly created pages with [[slug]] citations
   • Note: "Researched fresh and saved to wiki."
5. Offer to save analysis as a wiki page if the answer is substantial

Quick depth

• Read .wiki/index.md only
• Scan for matching slugs/titles by text match
• Return: list of relevant pages with one-line descriptions
• No page content read, no research fallback — fastest possible response
• If not found, suggest running standard /wiki-read

Deep depth

Everything in standard, plus:

• Search .wiki/raw/ for source materials matching the query
• Cross-reference raw sources with compiled pages
• Use all available tools iteratively for multi-channel research if needed
• Most thorough — uses the most context

Tool Discovery

This skill works with whatever tools the user has. No hardcoded channels or services.

Available tools are discovered at runtime:

• WebSearch / WebFetch — Claude Code built-in web search and fetch
• Any MCP search tools (Perplexity, Exa, Tavily, etc.)
• bin/search-academic.py — Semantic Scholar, arXiv, OpenAlex, CrossRef (if available)
• bin/search-code.py — GitHub, npm, PyPI, Stack Overflow (if available)
• bin/fetch.py — content extraction chain

The skill uses whatever is available — if the user has Perplexity MCP, it gets used. If they only have WebSearch, that works too.