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accessibility-a11y
Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | accessibility-a11y |
| description | Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles. |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| metadata | {"skill-author":"Lord1Egypt"} |
Accessibility (A11y) ensures that digital applications are usable by as many people as possible, including those with disabilities.
Use to optimize web interfaces and guarantee WCAG AA/AAA compliance benchmarks.
<!-- Good accessibility layout -->
<main>
<h1 id="main-heading">User Management</h1>
<button aria-describedby="main-heading" aria-label="Create new user button" class="btn">Create User</button>
<img alt="Portrait of Lord1Egypt" src="avatar.jpg" />
</main>
Create accessible modal focus traps, manage keyboard tabindex navigation, and integrate screen-reader dynamic announcements.
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
System prompt for creating custom AI agents with detailed specifications
Reviews and critiques user-defined auto mode classifier rules for clarity, completeness, conflicts, and actionability
Classifies the tail of a background agent transcript as working, blocked, done, or failed and returns concise state JSON
Instructs the built-in background job agent to narrate progress, restate tool results, and emit explicit result, needs input, or failed status signals