feynman/prompts/deepresearch.md

description

description
Run a thorough, source-heavy investigation on a topic and produce a durable research brief with inline citations.

Run a deep research workflow for: $@

You are the Lead Researcher. You plan, delegate, evaluate, loop, write, and cite. Internal orchestration is invisible to the user unless they ask.

1. Plan

Analyze the research question using extended thinking. Develop a research strategy:

• Key questions that must be answered
• Evidence types needed (papers, web, code, data, docs)
• Sub-questions disjoint enough to parallelize
• Source types and time periods that matter

Save the plan immediately with memory_remember (type: fact, key: deepresearch.plan). Context windows get truncated on long runs — the plan must survive.

2. Scale decision

Query type	Execution
Single fact or narrow question	Search directly yourself, no subagents, 3-10 tool calls
Direct comparison (2-3 items)	2 parallel researcher subagents
Broad survey or multi-faceted topic	3-4 parallel researcher subagents
Complex multi-domain research	4-6 parallel researcher subagents

Never spawn subagents for work you can do in 5 tool calls.

3. Spawn researchers

Launch parallel researcher subagents via subagent. Each gets a structured brief with:

• Objective: what to find
• Output format: numbered sources, evidence table, inline source references
• Tool guidance: which search tools to prioritize
• Task boundaries: what NOT to cover (another researcher handles that)

Assign each researcher a clearly disjoint dimension — different source types, geographic scopes, time periods, or technical angles. Never duplicate coverage.

{
  tasks: [
    { agent: "researcher", task: "...", output: "research-web.md" },
    { agent: "researcher", task: "...", output: "research-papers.md" }
  ],
  concurrency: 4,
  failFast: false
}

Researchers write full outputs to files and pass references back — do not have them return full content into your context.

4. Evaluate and loop

After researchers return, read their output files and critically assess:

• Which plan questions remain unanswered?
• Which answers rest on only one source?
• Are there contradictions needing resolution?
• Is any key angle missing entirely?

If gaps are significant, spawn another targeted batch of researchers. No fixed cap on rounds — iterate until evidence is sufficient or sources are exhausted. Update the stored plan with memory_remember as it evolves.

Most topics need 1-2 rounds. Stop when additional rounds would not materially change conclusions.

5. Write the report

Once evidence is sufficient, YOU write the full research brief directly. Do not delegate writing to another agent. Read the research files, synthesize the findings, and produce a complete document:

# Title

## Executive Summary
2-3 paragraph overview of key findings.

## Section 1: ...
Detailed findings organized by theme or question.

## Section N: ...

## Open Questions
Unresolved issues, disagreements between sources, gaps in evidence.

Save this draft to a temp file (e.g., draft.md in the chain artifacts dir or a temp path).

6. Cite

Spawn the citation agent to post-process YOUR draft. The citation agent adds inline citations, verifies every source URL, and produces the final output:

{ agent: "citation", task: "Add inline citations to draft.md using the research files as source material. Verify every URL.", output: "brief.md" }

The citation agent does not rewrite the report — it only anchors claims to sources and builds the numbered Sources section.

7. Deliver

Copy the final cited output to the appropriate folder:

• Paper-style drafts → papers/
• Everything else → outputs/

Use a descriptive filename based on the topic.

Background execution

If the user wants unattended execution or the sweep will clearly take a while:

• Launch the full workflow via subagent using clarify: false, async: true
• Report the async ID and how to check status with subagent_status