Borrowing it
Nothing to install: this file belongs to theredbluepill/arc-interactive. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/theredbluepill/arc-interactive/main/AGENTS.mdgit 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/instructions/theredbluepill/arc-interactive/agents-md)<a href="https://agentmods.dev/instructions/theredbluepill/arc-interactive/agents-md"><img src="https://agentmods.dev/badge/instructions/theredbluepill/arc-interactive/agents-md/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/instructions/theredbluepill/arc-interactive/agents-md"><img src="https://agentmods.dev/badge/instructions/theredbluepill/arc-interactive/agents-md.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 | $0.05009 | $0.05009 |
| Opus 5.5 | $0.02004 | $0.02004 |
| Sonnet 5.5 | $0.01002 | $0.01002 |
| Haiku 4.5 | $0.00501 | $0.00501 |
Grade A, and why
arc-interactive AGENTS.md 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 2mo 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 — 467 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARCAGI-3 Game Designer Agent
Role
Agent responsible for designing and implementing ARC-AGI-3 games.
Benchmark principle
ARC-AGI-style benchmarks are about whether an AI can solve puzzles that humans can solve when both operate under the same official interface: defined actions, observation format, and stated game rules. The goal is to measure general problem-solving, not which system had the most task-specific training data or memorized solutions.
When you author games in this repo, treat human solvability (given that shared spec) as a design guide: prefer well-posed levels—where goals are clear from layout, mechanics, or fair in-observation cues—over under-specified puzzles that only yield to guessing, huge search, or spoilers absent from the agent’s observation.
Workflow
1. Game Design Phase
Input: Game concept or requirements Output: Game specification
Key Questions:
- Grid size? (8x8, 16x16, 24x24, 64x64)
- What entities? (player, targets, walls, hazards)
- What actions? (define what ACTION1-7 mean for your game)
- Win/lose conditions?
2. Implementation Phase
Input: Game specification
Output: Working game in environment_files/
Steps:
- Create directory:
environment_files/{stem}/{version}/(two-letter stem + digits, e.g.ez01; version folder is usuallyv1on first landing, then often an 8-char git prefix after CI — seeCONTRIBUTING.md) - Implement
{stem}.pywith:- Sprite definitions
- Static levels (no PCG)
- Game class extending
ARCBaseGame - Win/lose conditions
- Test with:
arc.makeusing the fullgame_idfrom that folder’smetadata.json, or locallyuv run python run_game.py --game {stem} --version auto— add--mode humanfor pygame hand-play (scripts/human_play_pygame.py).
3. Documentation Phase
Input: Completed game Output: Updated tracking files
Steps:
- Add entry to
GAMES.mdwith all metadata columns - If you discover a reusable pattern (not stem-specific), add a short bullet under Lessons learned (cross-repo) below; otherwise rely on
GAMES.md,{stem}.py, andscripts/render_arc_game_gif.py(see skillgenerate-arc-game-gifinskills/) - Optional: add
assets/{stem}.gifusing the generate-arc-game-gif skill (advancing levels + 1–2 fail clips, HUD inRenderableUserDisplay)
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.
- 2mo ago First seen · 467 lines · 5,009 tokens per session scan A 9e15177911f6
arc-interactive AGENTS.md is an instructions file published in the GitHub repository theredbluepill/arc-interactive (57 stars, last pushed 1mo ago), licensed MIT. It adds 5,009 tokens to every session, about $0.0200 per session on Opus 5.5. 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 instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
generative-ai-for-beginners AGENTS.md
AGENTS.md instructions for microsoft/generative-ai-for-beginners, covering agents.md, project overview, setup commands, initial repository setup and clone the repository.