accessibility
FreeEnsure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Astro framework expert including components, content collections, and integrations
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See how to installReview the instructions the agent will receive before you install.
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Build and Deployment
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Component Development
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Content Management
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Data Fetching
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Key Principles
This expert skill consolidates 1 individual skills:
defineCollection() + schema validation catches frontmatter errors at build time, not at runtime in production.client:load, client:idle, or client:visible intentionally; never rely on defaults; wrong hydration strategy defeats Astro's performance model.<Image> or getImage() from astro:assets; raw <img> tags skip width/height inference, format conversion, and LCP optimization.Astro.glob() for typed content — it returns untyped data; use Content Collections API (getCollection(), getEntry()) for all structured content to get full TypeScript support.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Using React/Vue for static-only UI | Ships unnecessary JS; kills Astro's zero-JS default | Use .astro components for static content |
Raw <img> tags instead of <Image> | Misses LCP optimization, format conversion, lazy loading | Use <Image> from astro:assets always |
| Content without Zod schema | Frontmatter errors surface at runtime, not build time | Define schema in src/content/config.ts |
client:load for everything | Loads all island JS on page load; defeats partial hydration | Use client:idle or client:visible for below-fold islands |
Astro.glob() for structured content | Returns untyped; no schema validation | Use getCollection() from astro:content |
Before starting:
cat .claude/context/memory/learnings.md
After completing: Record any new patterns or exceptions discovered.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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