Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add theredbluepill/arc-interactive --skill play-arc-gamegit clone --depth 1 https://github.com/theredbluepill/arc-interactiveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/theredbluepill/arc-interactive/play-arc-game)<a href="https://agentmods.dev/skills/theredbluepill/arc-interactive/play-arc-game"><img src="https://agentmods.dev/badge/skills/theredbluepill/arc-interactive/play-arc-game/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/theredbluepill/arc-interactive/play-arc-game"><img src="https://agentmods.dev/badge/skills/theredbluepill/arc-interactive/play-arc-game.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01487 |
| Opus 5 | $0.00000 | $0.00744 |
| Sonnet 5 | $0.00000 | $0.00297 |
| Haiku 4.5 | $0.00000 | $0.00149 |
Grade A, and why
play-arc-game scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Play ARC Game Skill
Skill for playing and testing ARC-AGI-3 games using run_game.py.
Running Games
(<stem> is the two-letter+digits id from GAMES.md, e.g. ez01. --version auto picks the sole package dir under environment_files/<stem>/.)
Random-agent mode (default)
Omit --mode or pass --mode random-agent — runs --steps random picks among ACTION1–ACTION5 (default 100 steps).
uv run python run_game.py --game <stem> --version auto --mode random-agent --steps 50
Terminal mode (typed 1–7)
uv run python run_game.py --game <stem> --version auto --mode terminal
Hand-play (pygame window)
Add --mode human — opens scripts/human_play_pygame.py (WASD / arrows, click for ACTION6 in display space). There is no separate matplotlib player in this repo.
Programmatic Testing
arc.make needs the full game_id string from that package’s metadata.json (e.g. ez01-63be02fb). From repo root you can resolve it with scripts/env_resolve.full_game_id_for_stem("ez01").
from arc_agi import Arcade, OperationMode
from arcengine import GameAction
arc = Arcade("environment_files", OperationMode.OFFLINE)
env = arc.make("ez01-63be02fb", seed=0) # replace with your tree’s metadata game_id
# Execute actions
result = env.step(GameAction.ACTION1, reasoning={"test": 0})
print(f"State: {result.state}")
Other stems: pb02 (two-crate push), nw01 (arrow-tile forcing), ex01 (uses ACTION5 on the exit pad), gp01 / lo01 (ACTION6-only play; ACTION1–4 are no-ops) — each uses its own game_id from disk.
ACTION6: Click/Coordinate Actions
For games using ACTION6 (click), coordinates are in display space (0-63), not grid space.
Display to Grid Conversion
For a 32×32 grid on a 64×64 camera:
# Grid to display (what you send in action.data)
display_x = grid_x * 2 + 1
display_y = grid_y * 2 + 1
# Display to grid (what you receive)
grid_x = (display_x - 1) // 2
grid_y = (display_y - 1) // 2
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 146 lines · 0 tokens per session scan A 118b5ba04831
play-arc-game is a skill published in the GitHub repository theredbluepill/arc-interactive (54 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,487 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gameobject-component-destroy
Destroy one or more Components from a target GameObject. Missing (null) components are skipped — they cannot be destroyed. Use 'gameobject-find' and 'gameobject-component-get' to identify the components first.
unity-version-split
Split a C# file into Unity 6.5+ and pre-Unity 6.5 variants. Use when a file needs different implementations for different Unity versions due to API changes (e.g., EntityId vs int, GetEntityId vs GetInstanceID).
unity-addressables
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motion
How an agent turns a character mesh into a usable animated FBX — and how to judge whether the result is shippable.
playtest-report
Generates a structured playtest report template or analyzes existing playtest notes into a structured format. Use this to standardize playtest feedback collection and analysis.
unity-manual-component
Manually add, configure, reorder, and copy components on GameObjects using Unity Editor UI. For one-off Inspector workflows that do not need REST automation.