Borrowing it
Nothing to install: this file belongs to ukanwat/aaabench. 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/ukanwat/aaabench/main/.claude/skills/input-systems/SKILL.mdgit clone --depth 1 https://github.com/ukanwat/aaabenchWrote 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/ukanwat/aaabench/input-systems)<a href="https://agentmods.dev/skills/ukanwat/aaabench/input-systems"><img src="https://agentmods.dev/badge/skills/ukanwat/aaabench/input-systems/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/ukanwat/aaabench/input-systems"><img src="https://agentmods.dev/badge/skills/ukanwat/aaabench/input-systems.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 | $0.00088 | $0.01913 |
| Opus 5.5 | $0.00035 | $0.00765 |
| Sonnet 5.5 | $0.00018 | $0.00383 |
| Haiku 4.5 | $0.00009 | $0.00191 |
Grade A, and why
input-systems 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.
This is a copy
92% identical to input-systems — 89 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Input systems
Never wire gameplay to raw keys. Map physical inputs (a key, a button, a touch)
to named actions (jump, interact, move), and let gameplay read actions.
That one indirection gives you rebinding, multi-device support, and accessibility
almost for free. This skill is the engine-neutral architecture; bind it to
unity-input-system, unreal-enhanced-input, or Godot's InputMap.
When to use
- Use to design an input layer: actions, bindings, multiple devices, and a rebinding UI with conflict detection and saved bindings.
- Use to add analog handling (deadzones, sensitivity) and game-feel features (input buffering, coyote time).
- Use to make controls accessible (full remapping, hold-vs-toggle, sensitivity, no required simultaneous presses).
When not to use: for an engine's concrete input package/API, use
unity-input-system, unreal-enhanced-input, or Godot's InputMap. For the
movement/jump physics the buffer feeds, see physics-tuning and the engine
movement skill. Persisting bindings to disk is save-systems.
Core workflow
- Define actions, not keys. Gameplay asks "is
jumppressed?", never "is Space pressed?". Actions are the stable contract; bindings are data. - Bind per device. Each action holds bindings for keyboard, gamepad, and touch. The active device is whichever last sent input; swap UI prompts to match.
- Read the right edge. Use pressed-this-frame (edge) for discrete actions (jump, interact) and held (level) for continuous ones (move, aim). Confusing the two causes double-fires or missed presses.
- Filter analog input. Apply a deadzone to sticks/triggers so resting drift reads as zero, and scale sensitivity/curve to taste.
- Buffer for feel. Remember a pressed action for a short window so a slightly early press still fires (input buffering); allow a jump shortly after leaving a ledge (coyote time).
- Make rebinding first-class. A UI that captures the next input, detects
conflicts, and persists bindings — and a reset-to-default. Save via
save-systems. - Verify on every device and with rebinds: keyboard, gamepad, touch; rebind an action mid-game and confirm gameplay and prompts follow.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 163 lines · 88 tokens per session scan A 457e526c24f1
input-systems is a skill published in the GitHub repository ukanwat/aaabench (385 stars, last pushed 2mo ago), licensed MIT. It adds 88 tokens to every session and 1,913 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to input-systems, differing in 89 lines, and is treated as a copy.
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