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 perniemann/pnCore --skill pn-game-logicgit clone --depth 1 https://github.com/perniemann/pnCoreWrote 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/perniemann/pncore/pn-game-logic)<a href="https://agentmods.dev/skills/perniemann/pncore/pn-game-logic"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-game-logic/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/perniemann/pncore/pn-game-logic"><img src="https://agentmods.dev/badge/skills/perniemann/pncore/pn-game-logic.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 | $0.00042 | $0.01048 |
| Opus 5.5 | $0.00017 | $0.00419 |
| Sonnet 5.5 | $0.00008 | $0.00210 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
pn-game-logic 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 1mo 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game logic skill
When to use
- Implementing or refactoring game loop architecture
- Managing game states (menu, play, pause, gameover)
- Handling input (keyboard, mouse, gamepad)
- Implementing collision detection or spatial partitioning
- Adding scoring, progression, or save/load
- Structuring game logic for testability
Game loop
- Fixed timestep: Use a fixed delta (e.g. 1/60s) for physics and deterministic logic. Accumulate elapsed time and step in fixed increments to avoid spiral of death.
- Accumulator pattern:
accumulator += delta; while (accumulator >= FIXED_STEP) { update(); accumulator -= FIXED_STEP; }. Cap accumulator to avoid large catch-up steps. - Render interpolation: For smooth visuals, interpolate between last and current physics state using
accumulator / FIXED_STEPwhen rendering. - Single loop: Use one render loop with
requestAnimationFrame. Separateupdate(delta)(logic) fromrender()(draw).
State machines
- Game states: Model high-level states (menu, play, pause, gameover, settings). Use explicit state enum or state machine (e.g.
currentState,enter(),exit(),update()). - Transitions: Define valid transitions; guard against invalid state changes. Use events or callbacks for state entry/exit (e.g. play music on play, pause physics on pause).
- Sub-states: For complex flows (e.g. play → cutscene → play), use nested or hierarchical state machines when needed.
Input handling
- Action mapping: Map raw keys/buttons to logical actions (e.g. "jump", "move", "shoot"). Decouple input from game logic for rebinding and multiple devices.
- Input buffering: For responsive controls, buffer inputs for a short window (e.g. jump buffer so late jump still registers). Clear buffer on consume.
- Gamepad: Normalize axes and buttons across devices. Use
navigator.getGamepads()or a library (e.g. gamepad API wrapper) for consistent access. - Order: Process input → update logic → render. Avoid reading input mid-update.
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.
- 1mo ago First seen · 74 lines · 42 tokens per session scan A 936b5e54c48c
pn-game-logic is a skill published in the GitHub repository perniemann/pnCore (0 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 1,048 once invoked, about $0.0002 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 2026-09-03.
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