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 | $0.00021 | $0.00524 |
| Sonnet 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 8d 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.
- 8d 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 6d ago), licensed MIT. It adds 42 tokens to every session and 1,048 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
t-800-run-gates
A set of machine checks for deciding whether a T-800 run is ready to finish. It covers different run modes, such as creating, patching, looping, or auditing.
screen-reader-testing
Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues, or ensuring assistive technology support.
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
e2e-testing-patterns
Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when implementing E2E tests, debugging flaky tests, or establishing testing standards.
implement-universal
Harness-agnostic version of /implement. Drives a single workshop ticket through the SWE→Tester loop in ONE conversation, with the role prompts bundled as agents/software-engineer.md and agents/tester.md instead of being launched as subagents. Resolves the ticket from implementyourself/tasks/, creates an…
test
Enter the Test phase of CocoBrew. Reads spec.md test requirements, generates test cases, executes SQL validation and quality checks, records results in test.md. Can be re-run without full rebuild. Requires Build phase completion.