research

name	research
description	Multi-source research across code, discourse, and academic channels.
alwaysApply	false
category	orchestration
tags	["research","synthesis","multi-source"]
tools	[]
estimated_tokens	600
progressive_loading	true
orchestrates	["tome:code-search","tome:discourse","tome:papers","tome:triz","tome:synthesize"]
model_hint	standard

Research Session Orchestrator

Run a full multi-source research session: classify the domain, dispatch parallel agents, synthesize findings, and output a formatted report.

Workflow

Step 1: Classify Domain

Run the domain classifier on the topic:

from tome.scripts.domain_classifier import classify
result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights

If confidence < 0.6, ask the user to confirm or override the domain classification before proceeding.

Step 2: Plan Research

from tome.scripts.research_planner import plan
research_plan = plan(result)
# research_plan.channels, research_plan.weights, research_plan.triz_depth

Step 3: Create Session

from tome.session import SessionManager
mgr = SessionManager(Path.cwd())
session = mgr.create(topic, result.domain, result.triz_depth, research_plan.channels)

Step 4: Dispatch Agents

Launch research agents in parallel using the Agent tool. Use this mapping:

Channel	Agent Type	Prompt Includes
code	tome:code-searcher	topic
discourse	tome:discourse-scanner	topic, domain, subreddits
academic	tome:literature-reviewer	topic, domain
triz	tome:triz-analyst	topic, domain, triz_depth

Rules:

• Always dispatch code and discourse agents
• Dispatch academic agent only if "academic" is in research_plan.channels
• Dispatch triz agent only if "triz" is in research_plan.channels AND triz_depth != "light"
• Dispatch all eligible agents in a SINGLE message (parallel, not sequential)

Each agent prompt must include:

1. The topic string
2. The domain classification
3. Any channel-specific context (subreddits for discourse, triz_depth for triz)