Arc skill

Set the launcher once, work inside one directory per game, then start or resume:

ARC="<this-skill-directory>/scripts/arc"   # absolute path
mkdir -p <run-dir> && cd <run-dir>         # one directory = one run
"$ARC" start <GAME_ID>

start defaults to a local simulator with competition semantics: one logical run, append-only history, paid resets, completed levels never lost. It is crash-safe — after any interruption, rerun "$ARC" start <GAME_ID> and the run is resumed or replayed exactly. Use --mode competition (live remote server, ~15-minute idle lease, no replay recovery) only when the user explicitly asks. Never create a second run for the same game, never inspect the game's source or private state, and never edit .arc/ by hand except NOTES.md.

The game

A 64×64 board of 16 colors, hidden rules, several levels. Interface priors (not guarantees): ACTION1/2/3/4 = up/down/left/right, ACTION5 = interact, ACTION6:x,y = click at x=column y=row, ACTION7 = undo. Availability can change after every action.

Finish first. Historically every lost point came from unfinished games, not from extra actions. Early levels are usually cheap tutorials: a wrong action that teaches a mechanic beats a minute of deliberation. Act early, act often, learn from every grade. Efficiency only matters once finishing is likely.

The loop — look, predict, act, compare, note

1. Look: open the printed IMAGE; read the worded TRANSITION story.
2. Predict + act: every action requires --predict; the harness grades it:

"$ARC" act ACTION1 --predict "move 12,5 0,-1"
"$ARC" act ACTION6 3 14 --predict "cell 3,14=9; region 0:8,10:20" --because "test button"

3. Compare: read the ✓/✗ grade, the worded TRANSITION story, and the cell-exact DIFF before/after masks in the result (arc view re-renders them for any event). A ✗ is the most valuable thing that can happen — reality just corrected you for one action.
4. Note: keep .arc/NOTES.md to one page with three sections — Verified (cite event ids), Assumed / open questions, Plan. After a ✗, fix the notes before the next action. arc status prints the file in full, so it is also your recovery story after any context loss.

Claim vocabulary (full reference: "$ARC" act --help): noop, change, cell X,Y=V, move X,Y DX,DY, vanish X,Y, region X0:X1,Y0:Y1, level+1, win; several separated by ;. Coordinates are x=column, y=row, like ACTION6. Free text is allowed and merely claims "something changes" — prefer one specific claim; it grades sharper and teaches more.

Batching proven mechanics

Once a mechanic is verified, stop paying one command per step — batch with a claim on every step; execution halts at the first miss so a wrong theory cannot burn the rest of the queue:

"$ARC" commit \
  --step "ACTION4 :: move 12,5 1,0" \
  --step "ACTION4 :: move 13,5 1,0" \
  --step "ACTION1 :: level+1"

Batch only movement you can predict cell-exactly or level-exactly; never batch exploration.

Levels, consumables, reset

• Completing a level archives your notes to .arc/levels/. The new level may reuse mechanics — treat every earlier Verified claim as Assumed until it survives one test on the new board (status reminds you until the notes change).
• An object that vanished and never came back is a consumable. Spend consumables last, after reversible probes; before an irreversible-looking action, prefer ACTION7 (undo) tests when available.
• "$ARC" reset --because "<why this board is unrecoverable>" rewinds only the current level, for the price of one action. After GAME_OVER the reason may be omitted. Completed levels and history are never lost.

When a level resists — the rules tier (optional)

Status nudges you after many actions or repeated misses on one level. Then, and only then, escalate from prose to executable rules: write a plain rules.py (grounding, step, actions, goal), verify it against the entire recorded history, and let A* search find the plan. Each plan step carries its own prediction, so live execution still halts on the first surprise.

"$ARC" rules help      # the compact contract
"$ARC" rules init      # template; then edit rules.py
"$ARC" rules replay    # must fit or gap on every recorded transition
"$ARC" rules solve     # writes .arc/plan.json
"$ARC" commit @.arc/plan.json

Model only verified mechanics; mark everything else Unknown("why") — replay reports gaps honestly instead of pretending a fit. This tier is never required and never worth it before the game has taught you its mechanics.

Evidence tools

"$ARC" status                       # full picture + notes; run after context loss
"$ARC" view --grid                  # exact 0-f pixels
"$ARC" view --crop 8:24,10:30       # exact half-open crop
"$ARC" view --event 12 --frames     # animation frames of a past action
"$ARC" python 'connected_components(grid)'
"$ARC" python 'shortest_path((r0,c0),(r1,c1), passable_mask)'

arc python preloads every settled board (grid, previous, frames, transitions, actions), NumPy, perception helpers, BFS, and A* — free offline computation; use it for parsing and pathfinding instead of paid probing.

Install location

This folder is agent-agnostic. Copy it into the platform's auto-discovered skill directory (Claude Code: .claude/skills/arc-skill/, Codex: .agents/skills/arc-skill/). From an unrecognized location, instruct the agent to read this SKILL.md completely before starting.