plurigrid 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
globandGrepto find relevant files - Check
.md,.org,.jl,.py,.jsonfiles - 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 searchcrawling_exa- Extract web contentcompany_research_exa- Company researchdeep_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
- NEVER use
web_searchas a fallback - it's not equivalent to Exa - NEVER use
web_searchin Task subagents - use Exa tools instead - Always search local ~/.topos first - may have cached/annotated versions
- Use parallel subagents for academic MCPs to maximize speed
- 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
Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
Graph Theory
- networkx [○] via bicomodule
- Universal graph hub
Bibliography References
algorithms: 19 citations in bib.duckdb
Cat# Integration
This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
GF(3) Naturality
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.