depth-search

# Depth Search Comprehensive multi-source research skill. Searches across academic databases, semantic web search, and local knowledge before asking the user for help. ## Search Order Execute searches in this order, using parallel subagents where possible: ### 1. Local Knowledge Base (~/.topos) Search `~/.topos` directory first for existing research, notes, and cached data: - Use `glob` and `Grep` to find relevant files - Check `.md`, `.org`, `.jl`, `.py`, `.json` files - Look in subdirectories: `skills/`, `archived/`, `Gay.jl/`, etc. ### 2. Academic MCPs (parallel) Launch parallel subagents to search all 4 academic sources: | MCP | Tools | Best For | |-----|-------|----------| | **arxiv** | `search_papers`, `get_paper`, `download_paper` | Preprints, CS/physics/math papers | | **semantic-scholar** | `paper_relevance_search`, `paper_details`, `paper_citations` | Citation analysis, author profiles | | **paper-search** | `search_arxiv`, `search_pubmed`, `search_biorxiv`, etc. | Multi-source aggregation | | **deepwiki** | `read_wiki_structure`, `read_wiki_contents`, `ask_question` | GitHub repo documentation | ### 3. Exa Semantic Search Use Exa MCP for high-quality web search: - `web_search_exa` - Semantic web search - `crawling_exa` - Extract web content - `company_research_exa` - Company research - `deep_researcher_start` / `deep_researcher_check` - Deep research tasks ### 4. Ask User for Help If all sources fail to find what's needed: - **DO NOT fall back to `web_search`** - it's basic keyword matching only - Instead, ask the user: - "I couldn't find [X] in academic databases, Exa, or local files. Can you provide a link, paper title, or more context?" - Suggest specific sources they might check manually - Offer to try different search terms ## Critical Rules 1. **NEVER use `web_search` as a fallback** - it's not equivalent to Exa 2. **NEVER use `web_search` in Task subagents** - use Exa tools instead 3. **Always search local ~/.topos first** - may have cached/annotated versions 4. **Use parallel subagents** for academic MCPs to maximize speed 5. **Ask user for help** rather than guessing or using inferior search ## Example Workflow ``` User: "Find papers on world models for LLMs" 1. Search ~/.topos for existing notes/papers 2. Launch 4 parallel Task subagents: - arxiv: search_papers("world models LLM") - semantic-scholar: paper_relevance_search("world models language models") - paper-search: search across all sources - deepwiki: check relevant GitHub repos 3. If needed, use Exa: web_search_exa("world models LLM research") 4. Synthesize results from all sources 5. If still not found: ask user for clarification ``` ## Parallel Subagent Template When searching academic sources, use this pattern: ``` Launch 4 parallel Task subagents: - Task 1: Use arxiv MCP to search for [query] - Task 2: Use semantic-scholar MCP to search for [query] - Task 3: Use paper-search MCP to search for [query] - Task 4: Use deepwiki MCP to find related repos/docs ``` ## What NOT To Do ❌ `web_search` as fallback when Exa fails ❌ Single-source search when multiple are available ❌ Skipping local ~/.topos search ❌ Guessing answers without exhausting sources ❌ Sequential searches when parallel is possible ## What TO Do ✅ Search ~/.topos first for cached knowledge ✅ Parallel subagents for academic MCPs ✅ Exa for semantic web search ✅ Ask user when sources are exhausted ✅ Synthesize results from multiple sources