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FreeEnsure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Node.js backend expert including Express, NestJS, and async patterns
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When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
When reviewing or writing code, apply these guidelines:
This expert skill consolidates 1 individual skills:
typescript-expert - TypeScript type systems, patterns, and tooling for Node.js developmentclass-validator or zod — unvalidated inputs reach business logic, enabling injection attacks and data corruption; validation at the boundary is the last line of defense.async/await with try/catch is the required standard for all async operations.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Missing DTO validation at API boundary | Unvalidated inputs enable injection and data corruption | Use class-validator on DTOs; reject invalid inputs at the controller boundary |
| Using callbacks in new code | Error propagation non-deterministic; unhandled rejections crash the process | Use async/await with try/catch for all async operations |
| No global exception filter in NestJS | Unhandled exceptions expose raw stack traces to clients | Add a global ExceptionFilter that logs internally and returns sanitized error responses |
| Synchronous operations on the event loop | Blocks all concurrent requests; latency spikes under any load | Use async alternatives (fsPromises, streams); offload CPU-bound work to worker threads |
| New database connection per request | Exhausts connection limit under load; adds connection overhead latency | Pool connections at startup with pg.Pool, Knex, or ORM connection pooling |
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
The English summary is being prepared. Showing the original description.
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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