Skill UI

Analyzes SQL statements, identifies bottlenecks, suggests indexes and rewrites, and leverages the sql-query-optimizer plugin so you can improve database responsiveness when queries run slowly.

Orchestrates complex multi-stage deployment pipelines by generating production-ready configs and setup code based on user requirements, ideal for automating CI/CD and reliable software releases.

This skill automates the entire software development process overnight. By leveraging mandatory Git hooks, it strictly enforces Test-Driven Development (TDD). Use this when complex features need building, large codebases need refactoring, or bugs need fixing autonomously, guaranteeing that every commit passes rigorous testing cycles and meets high production-ready standards.

Covers HuggingFace PEFT workflows for LoRA/QLoRA-based LLM fine-tuning, enabling sub-1% parameter updates on limited GPUs plus adapter serving tips.

Guides teams through automated performance baseline creation, offering templates, validation rules, and best-practice checklists for load, stress, and benchmarking workflows within performance testing efforts.

Automates load, stress, spike, and endurance tests through the performance-test-suite plugin, collecting response time, throughput, and error-rate metrics to spot CPU/memory/database/network bottlenecks and deliver graphs plus optimization guidance.

Runs automated visual regression tests via Percy, Chromatic, or BackstopJS to capture component or page screenshots, compare them against baselines, and flag unintended UI changes during feature updates or CI/CD runs.

A comprehensive guide to implementing Perplexity's API integration at various scales. It provides three validated architectural blueprints—Direct Widget, Cached Research Layer, and Multi-Query Pipeline—allowing developers to choose the optimal structure based on required query volume, latency goals, and complexity. Learn how to scale from simple search widgets to automated, high-volume research pipelines.

This guide details how to implement Role-Based Access Control (RBAC) for Perplexity API in enterprise environments. Since Perplexity lacks native RBAC, the solution involves building a middleware gateway. This gateway enforces granular policies, including team-specific API key usage, model restriction (e.g., limiting access to 'sonar' only), budget caps, rate limiting, and mandatory search domain filtering for compliance and security.

This guide implements comprehensive guardrails for Perplexity Sonar API. It covers crucial aspects like query content moderation, PII sanitization, enforcing user usage quotas, selecting appropriate models based on user tiers, and scoring citation reliability to ensure safe, ethical, and cost-effective AI usage.

A comprehensive guide and implementation examples for handling API rate limits (HTTP 429) when interacting with the Perplexity Sonar API. This resource provides multiple robust techniques, including exponential backoff with jitter, queue-based rate limiting, and custom Token Bucket implementation, ensuring reliable and high-throughput API calls for developers using TypeScript and Python.

This comprehensive guide assists developers in migrating their code and API calls when Perplexity updates its model generations or changes API parameters. It provides a clear map from legacy models (pplx-api) to the current Sonar family (sonar, sonar-pro), covering model name changes, base URL updates, parameter adjustments, and SDK upgrade steps. Use this guide whenever you encounter deprecation warnings or need to ensure compatibility with the latest Perplexity Sonar models.