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 check-arc-game-discoverablegit 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/check-arc-game-discoverable)<a href="https://agentmods.dev/skills/theredbluepill/arc-interactive/check-arc-game-discoverable"><img src="https://agentmods.dev/badge/skills/theredbluepill/arc-interactive/check-arc-game-discoverable/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/check-arc-game-discoverable"><img src="https://agentmods.dev/badge/skills/theredbluepill/arc-interactive/check-arc-game-discoverable.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 | $0.00053 | $0.02559 |
| Opus 5 | $0.00026 | $0.01280 |
| Sonnet 5 | $0.00011 | $0.00512 |
| Haiku 4.5 | $0.00005 | $0.00256 |
Grade A, and why
check-arc-game-discoverable 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 10d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Check ARC game discoverability by play (no prior)
Soul of this skill
Assume a participant (human or AI) who has never seen this task, has not been trained on it, and receives no tutorial text, no labeled rule sheet, and no external description of goals or what each action “means.”
That is by design. In benchmark-style play, goals, rules, and action semantics are meant to be hidden from the game taker at the start. Participants are expected to figure them out themselves during play—by trying actions, watching what changes, hitting win/lose, and iterating.
Solvable here means exactly that: solvable by discovery through play—not “everything was explained or drawn on the HUD up front.” A stem passes if a reasonable participant can converge on enough of the goal and mechanics to win from experience alone (same observation and action channel an agent gets). It fails if, even while playing, the information or feedback needed to win never becomes available, or the space is so flat that learning cannot get off the ground.
If a human can do that, it is the foundation for claiming an AI without task-specific prior could in principle do the same under the same interface: the task must be learnable from interaction, not from documentation.
Human first, AI co-required
Under ARC-style benchmarking, human-solvable (in this skill’s sense) and AI-solvable under the official interface are meant to co-exist:
-
Human first — Primary gate: can a cold-start participant learn goal, critical state, and action effects through play (observation + legal actions + consequences)? Review that discovery path, not whether the author pasted rules on screen.
-
AI co-required — Benchmark intent: human-solvable ⇒ AI-solvable (in principle) under the same official observation and action interface. Passing the human bar implies the task is fair for an agent that sees that channel: no extra win-critical information for humans only, levels mechanically winnable under the game’s actual logic, and no reliance on prose outside the env. It does not guarantee a particular model will win—only that the spec is not rigged against an honest AI relative to the human.
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.
- 10d ago First seen · 131 lines · 53 tokens per session scan A 18f0ee6bba9c
check-arc-game-discoverable is a skill published in the GitHub repository theredbluepill/arc-interactive (54 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 2,559 once invoked, about $0.0003 per session on Opus 5. 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
Manage Addressables groups, entries, profiles and content builds (com.unity.addressables, reflection-based).
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.