deep-research

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Multi-stage research pipeline with evidence-quality ratings and citations. Classifies intent, clarifies scope, then routes to shallow (single-pass) or deep (parallel fan-out) research. Composes with Parallel AI MCP when available.

AI & Automation 36 stars 2 forks Updated today MIT

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Quality Score: 80/100

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# Deep Research Pipeline Host-side only (Claude Code). See `docs/agent-agnostic-debt.md` AAG-012 for backend parity status. A 4-stage research pipeline. Classifies research depth, clarifies scope when ambiguous, then routes to shallow or deep retrieval with structured citation-backed output. **Design pattern: Mediator.** This skill is the central coordinator. Research scouts and brainstormer are independent peers invoked through the Agent tool without cross-coupling. Neither agent knows about the other or about the pipeline stages. Composes existing infrastructure — does NOT duplicate it: - **Research scout agents** for evidence-classified source finding (deep path) - **brainstormer** agent for creative synthesis (when the user wants ideas, not just facts) - **Parallel AI MCP** for web search when available (graceful fallback to WebSearch) - **Vault memory** for prior decisions and research - **NotebookLM** for querying existing notebooks when relevant ## Instructions When the user invokes `/deep-research <topic>` or a trigger phrase matches: ### Stage 1: Classify intent Read the query and classify into one of: | Depth | Signal | Example | |-------|--------|---------| | **SHALLOW** | Single fact, narrow scope, recent event, quick comparison | "What's the latest on X?", "Compare A vs B briefly" | | **DEEP** | Multi-source synthesis, historical overview, regulatory landscape, decision brief, "research X" | "Research the regulatory landscape for Y", "Produce a brief on ...

Details

Author
sliamh11
Repository
sliamh11/Deus
Created
2 months ago
Last Updated
today
Language
Python
License
MIT

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