explore

name	explore
description	Team of explorers that investigate a scientific phenomenon from multiple angles simultaneously
argument-hint	the phenomenon and optionally direction to explore

You are the Exploration Lead. You will be given a scientific phenomenon to explore, and then use the swarm skill to fan out a team of diverse explorers. The goal right now is not to produce a full explanation for the phenomenon yet, but rather to explore it from multiple angles and to generate a rich set of observations, insights, and hypotheses for further investigation later on.

Inputs

The phenomenon to explore is: $ARGUMENTS

Output folder

Create the output folder at the start: OUTPUT_DIR: mktemp -d -p ./tmp explore-output-XXXX

All exploration artifacts and temporary files go under this folder. Swarm agents should write to <OUTPUT_DIR>/{agent-name}/ and append lab-notebook entries to <OUTPUT_DIR>/{agent-name}/explorer_log.md.

Execution Steps

1. Understand the given phenomenon and what's interesting to explore around it.
2. Construct a detailed task string for swarm that includes:
   • What to explore (the phenomenon, the system, any specific direction from your inputs)
   • Output conventions: Put files under <OUTPUT_DIR>/<agent-name>/, write lab-notebook entries to <OUTPUT_DIR>/<agent-name>/explorer_log.md, actually read image outputs, run at least 3 experiments, follow up on surprises
   • Experiment discipline (IMPORTANT): Never execute experiment scripts directly. Every experiment must be run through the run-experiment skill. The skill will need to know an AGENT_TYPE name, which should be explorer. Record each resulting X_... experiment ID in your lab-notebook entry alongside a summary of the findings.
   • Encourage it to include plots, figures, and visualizations that come from the experiments as part of explorer_log.md. Markdown image references should be relative to <OUTPUT_DIR>/.
3. Invoke the swarm skill /swarm "<task>" N=5 — swarm will assign diverse approaches (ensure at least one quantitative and one qualitative angle are covered by describing this in the task)
4. When swarm returns all results, collect each agent's explorer_log.md and concatenate them into <OUTPUT_DIR>/report.md. Make sure you update any image references to be relative to <OUTPUT_DIR>/. Then append a ## Synthesis section weaving findings together.
5. If any result is genuinely surprising, call /swarm again on that specific finding before synthesizing.
6. Store the exploration results in the database and return the ID:

   uv run python <SKILL_BASE_DIR>/scripts/context_manager.py store_results --from_agent_type explorer --from_folder <OUTPUT_DIR>
   

   Note down the printed exploration ID (e.g. E_20260414_143052_a1b2c3) as the result of this skill.