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 almasumdev/awesome-mobile-accessibility-agent-skills --skill keyboard-and-hardware-inputgit clone --depth 1 https://github.com/almasumdev/awesome-mobile-accessibility-agent-skillsWrote 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/almasumdev/awesome-mobile-accessibility-agent-skills/keyboard-and-hardware-input)<a href="https://agentmods.dev/skills/almasumdev/awesome-mobile-accessibility-agent-skills/keyboard-and-hardware-input"><img src="https://agentmods.dev/badge/skills/almasumdev/awesome-mobile-accessibility-agent-skills/keyboard-and-hardware-input/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/almasumdev/awesome-mobile-accessibility-agent-skills/keyboard-and-hardware-input"><img src="https://agentmods.dev/badge/skills/almasumdev/awesome-mobile-accessibility-agent-skills/keyboard-and-hardware-input.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 | $0.00043 | $0.01567 |
| Opus 5 | $0.00022 | $0.00783 |
| Sonnet 5 | $0.00009 | $0.00313 |
| Haiku 4.5 | $0.00004 | $0.00157 |
Grade A, and why
keyboard-and-hardware-input 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 203 lines · 43 tokens per session scan A cf74ce5db7a5
keyboard-and-hardware-input is a skill published in the GitHub repository almasumdev/awesome-mobile-accessibility-agent-skills (1 stars, last pushed 4mo ago), with no licence file. It adds 43 tokens to every session and 1,567 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 2026-09-01.
Other skills, from other repositories
iOS SwiftUI Accessibility
Enforces WCAG 2.2 accessible coding patterns when writing SwiftUI — labels, traits, Dynamic Type, contrast, touch targets, focus management, orientation, and more. Based on the ios-swiftui-accessibility-techniques project with 41 static analysis rules across 23 WCAG criteria.
biometric-auth
Biometric authentication on mobile using BiometricPrompt and LAContext, with keys cryptographically gated by user biometry. Use when adding unlock, re-auth, or step-up auth flows.
mfa-on-mobile
Multi-factor authentication on mobile — TOTP, push-based MFA, and recovery UX. Use when adding or reviewing a second factor.
oauth-mobile
OAuth 2.1 + PKCE for native mobile apps. Covers redirect URIs, AppAuth libraries, and the flows that are safe vs deprecated. Use when implementing or reviewing user login.
anti-tampering
Anti-tampering on mobile — signature checks, runtime application self-protection (RASP), and realistic return on investment. Use to decide which integrity controls are worth shipping.
code-obfuscation
Code obfuscation on mobile — R8 / ProGuard on Android, SwiftShield and its limitations on iOS, and realistic expectations. Use when hardening release builds.