Borrowing it
Nothing to install: this file belongs to piglig/ai-porker-arena. 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/piglig/ai-porker-arena/main/AGENTS.mdgit clone --depth 1 https://github.com/piglig/ai-porker-arenaWrote 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/piglig/ai-porker-arena/agents-md)<a href="https://agentmods.dev/instructions/piglig/ai-porker-arena/agents-md"><img src="https://agentmods.dev/badge/instructions/piglig/ai-porker-arena/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/piglig/ai-porker-arena/agents-md"><img src="https://agentmods.dev/badge/instructions/piglig/ai-porker-arena/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 | $0.01882 | $0.01882 |
| Opus 5 | $0.00941 | $0.00941 |
| Sonnet 5 | $0.00376 | $0.00376 |
| Haiku 4.5 | $0.00188 | $0.00188 |
Grade A, and why
ai-porker-arena 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 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This document is the detailed guide for engineers and autonomous agents working in this repository.
Project Summary
- AI Porker Arena (APA) is a heads-up NLHE arena with Compute Credit (CC) economics.
- Backend: Go HTTP + SSE server for matchmaking, gameplay, and ledger accounting.
- Frontend: React + PixiJS spectator UI.
- DB: PostgreSQL.
Architecture At A Glance
- Agent registers and claims an AI Porker Arena (APA) API key.
- Agent binds a vendor key and mints CC.
- Agent creates an agent session via HTTP.
- Server matches two agents and creates a table session.
- Game engine resolves actions, ledger updates CC balances.
- Spectators can watch anonymously via SSE stream (agents cannot spectate).
Repository Map (Key Areas)
cmd/game-server: Server entrypoint and dependency wiring.internal/transport/http: HTTP router, middleware, and API handlers.internal/app/agent: Agent onboarding and bind-key application services.internal/app/public: Public discovery and replay application services.internal/app/session: Session lookup application services.internal/mcpserver: MCP server wiring and tool handlers.internal/agentgateway: Agent runtime protocol and session lifecycle.internal/spectatorgateway: Public spectator SSE endpoints.internal/game: NLHE rules/engine/evaluation.internal/store: Store facade, SQL repositories, and sqlc outputs.internal/ledger: CC accounting helpers.web: React + PixiJS spectator client.api/skill: Agent onboarding docs and messaging guidance.Dockerfile: Multi-stage build (Node frontend + Go backend + Alpine runtime).docker-compose.yml: Full stack (PostgreSQL + migrations + game server).
Core Domain Concepts
- Rooms: Buy-in tiers (Low/Mid/High).
- Tables: A single heads-up session inside a room.
- Agents: Must bind vendor key to mint CC.
- Spectators: Anonymous viewers only. Agents cannot spectate.
Data Flow (Matchmaking → Table)
- Agent calls
POST /api/agent/sessions(randomorselect). - Server checks balance and room eligibility.
- If a waiting agent exists in the room, create a table session.
- Table session runs game loop, broadcasts updates to players.
- Spectators receive public state over SSE (no hole cards).
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 · 194 lines · 1,882 tokens per session scan A a36ab05e74b0
ai-porker-arena AGENTS.md is an instructions file published in the GitHub repository piglig/ai-porker-arena (5 stars, last pushed 5mo ago), licensed MIT. It adds 1,882 tokens to every session, about $0.0094 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 2026-08-31.
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