explore

name	explore
description	Use when user wants to explore a topic, find papers, map a field, or understand the research landscape. Not for explaining concepts — use learn for that.

If you were dispatched as a subagent to execute a specific search task, skip this skill. Do the task and return structured results immediately. Do NOT launch any searches until the user has confirmed the topic framing. One sentence of confirmation is enough. No searches before confirmation.

Explore Skill — Parallel + Persistent

Step 0 — Create session

Before anything else, create the session directory and open the lab notebook:

mkdir -p .research-loop/sessions/<slug>/

Where <slug> = topic lowercased, spaces to dashes, e.g. policy-compression-ai-agents.

Create .research-loop/sessions/<slug>/lab_notebook.md with:

# Lab Notebook — <topic>
Started: <date>
Status: exploring

---
## Session log

This file is the single source of truth for the entire research session. Every phase appends to it.

Step 1 — Confirm the topic

Say:

"I'll explore [topic]. Is this the right framing, or do you want to adjust it first?"

Wait for confirmation. Then append to lab_notebook.md:

## Framing
Topic: <confirmed topic>
Date: <date>
Researcher notes: <anything they said about prior knowledge>

Step 2 — Launch parallel searches

Spawn ALL four simultaneously. Each prompt must start with "You are a research search agent. Do not ask questions. Search and return structured results immediately."

Agent 1 prompt:
  You are a research search agent. Do not ask questions. Search and return structured results immediately.
  Task: Find the top 10 most important papers on [topic].
  Use web search. For each paper return: title, year, authors, 1-sentence contribution.
  Prioritize foundational and recent (2020–2025) papers. Return a clean numbered list.

Agent 2 prompt:
  You are a research search agent. Do not ask questions. Search and return structured results immediately.
  Task: Find active GitHub repos and benchmarks for [topic].
  Use web search. For each repo return: name, URL, stars (if available), 1-sentence description.
  Return 5–10 actively maintained repos.

Agent 3 prompt:
  You are a research search agent. Do not ask questions. Search and return structured results immediately.
  Task: Find 3 places where experts in [topic] fundamentally disagree.
  Use web search. For each debate: name it, state Side A's strongest argument, state Side B's strongest argument, explain why it matters.

Agent 4 prompt:
  You are a research search agent. Do not ask questions. Search and return structured results immediately.
  Task: Find explicit open problems stated in the [topic] literature.
  Use web search. Look for "future work" sections, unsolved challenges, gaps mentioned by field leaders.
  For each gap: state the problem, cite where it appears, explain why it's hard.
  Return 5–8 specific open problems.

Tell the user: Searching papers, repos, debates, and open problems in parallel...

Step 3 — Save everything, show synthesis

When all 4 return, append the full results to lab_notebook.md:

## Literature (Phase 1)
<full list of papers with title, year, contribution>

## Active Repos
<list of repos>

## Field Debates
<3 debates with both sides and strongest arguments>

## Stated Open Problems
<list of gaps found in literature>

Then show the user a synthesis (not the raw dump):

"Here's the landscape in 3 sentences: [synthesis]

The 3 most interesting angles I see: A) [angle] — [why interesting] B) [angle] — [why interesting] C) [angle] — [why interesting]

Which direction do you want to go deeper on?"

Wait for response. Append their choice to lab_notebook.md:

## Researcher direction choice
Chose: <their answer>
Reasoning they gave: <anything they said>

Step 4 — Extract mental models for chosen direction

Now spawn 1 focused search:

Agent: What are the 5 core mental models every expert in [chosen direction] carries?
       Not facts — the intuitions and ways of thinking that take years to develop.

Show the models conversationally, one at a time:

"Here's the first mental model experts in this space share: [model 1 + explanation]. Does this match your intuition?"

Append all 5 to lab_notebook.md:

## Mental Models
1. [name]: [description]
2. ...

Step 5 — Generate diagnostic questions

Say:

"Let me give you 3 questions that would expose whether someone truly understands this vs. memorized it. Try answering them — every wrong answer tells us something."

Show questions one at a time. Wait for answers. For each wrong answer:

"Here's what you're missing: [explanation]"

Append to lab_notebook.md:

## Diagnostic Q&A
Q1: [question]
Researcher answer: [their answer]
Expert answer: [correct answer]
Gap identified: [what they didn't know]
...

Step 6 — Transition

Say:

"Exploration complete. Lab notebook saved to .research-loop/sessions/<slug>/lab_notebook.md

Ready to find the gaps and run the Carlini gate? → /gap or just tell me which open problem interests you most."

What NOT to do

• Do NOT dump all papers at once
• Do NOT skip saving to lab_notebook.md after each phase
• Do NOT advance phases without user input