deep-research

name

deep-research

description

Read when the OPERATOR asks a question that will require web research across multiple sources — recommendations, comparisons, evaluations, multi-factor decisions, "what should I buy/use", or any question where you'd need to read several web pages. This skill decides whether to spawn a background research agent (to protect your context from heavy fetching) or handle it yourself. Do NOT read for simple factual lookups or questions fully answered by your knowledge base.

Deep Research Skill

You're reading this because the OPERATOR's question needs multi-source research.

Why the Agent Exists

Each web_fetch dumps thousands of tokens into your context. Doing several fetches inline to answer one question pollutes your main conversation — past messages get compressed, future turns get worse. The deep-research agent runs in an isolated context that is discarded after it delivers a summary. It protects your conversation while doing the heavy reading.

Decision Process

Step 1: Check knowledge

Call knowledge_search first. If you find existing notes or references that adequately answer the question, use them and respond directly. No agent needed.

Step 1.5: Consider reference import first

If the research centers on a specific tool, framework, or library (e.g. Ergogen, Dioxus, KiCad), check whether its official docs can be imported via the reference-import skill. Imported docs persist in your knowledge base and are searchable across sessions — far more valuable than ephemeral agent context. Read the reference-import skill, start the import in the background, and then optionally spawn the deep-research agent for the broader question (e.g. community guides, best practices) while the import runs.

Step 2: Spawn the agent

If knowledge didn't have a good answer (and reference import doesn't apply or is already running), spawn the deep-research agent. You matched this skill's description because the question needs multi-source research — that research belongs in the agent's isolated context, not inline.

Your next tool call after the knowledge check must be agent. Do not call web_search or web_fetch — every page you fetch inline is context you can never reclaim. Let the agent do the heavy reading.

Spawning the Agent

Use agent(action: 'start', name: 'deep-research', prompt: '...'). Include:

• Specific question — what exactly needs to be answered
• Context — constraints, preferences, use case the OPERATOR mentioned
• Scope — what sub-questions to investigate
• Recency — remind the agent to look for recent developments
• Known sources — if your knowledge base has source quality notes for this domain, pass them to the agent so it can skip the source-discovery phase
• Don't name specific options — describe the category, constraints, and use case, but do NOT pre-populate specific names, brands, or recommendations from your training data. Your knowledge is outdated — let the agent discover the current landscape through research. Bad: "looking at X, Y, and Z". Good: "looking for a [category] that meets [constraints]".

After spawning, tell the OPERATOR you've started a background research task and you'll share findings when it completes.

Follow-Up Questions

When the OPERATOR provides follow-up criteria or refinements after the agent delivered findings, continue the existing agent session:

agent(action: 'continue', agent_id: '<id>',
  prompt: '<new constraints and follow-up question>')

The agent already has full context from its research. Continuing lets it do targeted follow-up searches instead of starting from scratch. Never spawn a new agent for follow-ups on the same topic.