name	arcagi3-research-loop
description	Use when studying an ARC-AGI-3 game, planning the next remote attempt, or deciding how to learn the most from limited actions

ARC-AGI-3 Research Loop

Overview

Use this skill to turn vague curiosity about a game into a small, action-efficient experiment plan.

Core principle: every action should either advance the level or reduce uncertainty about the rules.

When to Use

Starting work on a new game
Planning the next remote attempt
Looking at a partially understood game and deciding what to test next
Trying to avoid wasting actions on blind exploration

Do not use this skill for low-level code refactors that do not change research behavior.

Loop

Restate the current game, known objective, and what remains uncertain.
Separate known mechanics from guessed mechanics.
Identify the single most valuable unknown.
Propose the smallest action sequence that could resolve that unknown.
Record what the attempt should teach, not just what it should do.
After the run, update the model of the game before planning another attempt.

What To Inspect

current action_space
recent observations and state transitions
scorecard and replay context
reasoning attached to prior actions
whether a failure was caused by misunderstanding or by poor execution

Action-Efficiency Rules

Prefer low-action probes over long uninformed sequences.
Do not spam actions to "see what happens."
Do not brute-force ACTION6 with blind coordinate sweeps.
If ACTION7 is available, consider whether controlled undo-based exploration is cheaper than restart-heavy exploration.
When taking a risky action, state what hypothesis it is testing.

Good Output

A good result from this skill is a short experiment note with:

current hypothesis
evidence already observed
next attempt plan
expected learning from success or failure

Common Mistakes

confusing activity with progress
mixing multiple hypotheses into one long attempt
failing to record what was learned from a run
ignoring changing action_space
treating replay artifacts as optional

ARC-AGI-3 Research Loop

Overview

Use this skill to turn vague curiosity about a game into a small, action-efficient experiment plan.

Core principle: every action should either advance the level or reduce uncertainty about the rules.

When to Use

Starting work on a new game

Planning the next remote attempt

Looking at a partially understood game and deciding what to test next

Trying to avoid wasting actions on blind exploration

Do not use this skill for low-level code refactors that do not change research behavior.

Loop

Restate the current game, known objective, and what remains uncertain.

Separate known mechanics from guessed mechanics.

Identify the single most valuable unknown.

Propose the smallest action sequence that could resolve that unknown.

Record what the attempt should teach, not just what it should do.

After the run, update the model of the game before planning another attempt.

What To Inspect

current action_space

recent observations and state transitions

scorecard and replay context

reasoning attached to prior actions

whether a failure was caused by misunderstanding or by poor execution

Action-Efficiency Rules

Prefer low-action probes over long uninformed sequences.

Do not spam actions to "see what happens."

Do not brute-force ACTION6 with blind coordinate sweeps.

If ACTION7 is available, consider whether controlled undo-based exploration is cheaper than restart-heavy exploration.

When taking a risky action, state what hypothesis it is testing.

Good Output

A good result from this skill is a short experiment note with:

current hypothesis

evidence already observed

next attempt plan

expected learning from success or failure

Common Mistakes

confusing activity with progress

mixing multiple hypotheses into one long attempt

failing to record what was learned from a run

ignoring changing action_space

treating replay artifacts as optional

arcagi3-research-loop