Nm Tome Research

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name: research description: Multi-source research across code, discourse, and academic channels version: 1.9.4 triggers: - research - synthesis - multi-source metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/tome", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: tome

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> Night Market Skill — ported from claude-night-market/tome. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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) 4. Instruction to return findings as JSON

Step 5: Collect and Synthesize

After all agents return:

1. Parse each agent's findings into Finding objects 2. Merge using tome.synthesis.merger.merge_findings() 3. Rank using tome.synthesis.ranker.rank_findings()

Step 6: Generate Output

from tome.output.report import format_report, format_brief, format_transcript
Default to report format
output = format_report(session)
Save to docs/research/
output_path = f"docs/research/{session.id}-{slug}.md"

Save the session state:

mgr.save(session)

Step 7: Present Results

Display a brief summary to the user:

Number of findings per channel

Top 3 findings by relevance

Path to saved report

Then offer interactive refinement: "Use /tome:dig \"subtopic\" to explore specific areas."

Error Handling

If an agent fails, continue with remaining agents

If all agents fail, report the error and suggest

manual research approaches

If synthesis produces 0 findings, state this clearly

rather than generating an empty report

Save session state even on partial failure

Output Format Selection

| Flag | Format | Function | |------|--------|----------| | (default) | report | format_report() | | --format brief | brief | format_brief() | | --format transcript | transcript | format_transcript() |