Skill UI

Behavioral rules for LLMs writing or reviewing code, emphasizing caution, simplicity, surgical edits, and goal-driven execution to avoid common mistakes during implementation tasks, bug fixes, or refactors.

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

A sophisticated writing editor designed to remove tell-tale signs of AI-generated text. This tool helps transform overly polished, robotic, or academic prose into natural, human-sounding writing. It detects and corrects patterns such as inflated symbolism, passive voice, vague attributions, and repetitive LLM structures, while also calibrating the output to match an author's specific voice and tone. Ideal for content review, editing, and improving natural communication flow.

A comprehensive guide for auditing and ensuring regulatory compliance (SOC2, GDPR, HIPAA) when integrating Large Language Models (LLMs) via OpenRouter. It covers best practices for data handling, access control, PII redaction, implementing robust audit trails, and configuring provider routing to maintain data sovereignty and meet strict industry standards.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

Runs external LLM CLIs (OpenAI Codex or Google Gemini) to review uncommitted changes, branch diffs, or specific commits, providing an independent second opinion before merges or releases.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

This skill autonomously improves generated academic papers by simulating a multi-round review, fixing, and recompilation cycle. It is designed to enhance writing quality, fix theoretical inconsistencies, and improve presentation structure after the initial drafting phase. It operates in rounds (default 2 rounds) using external LLM review and can optionally accept a style reference paper to guide structural fixes.

This skill automates the research refinement process by entering an autonomous, multi-round review loop. It leverages any OpenAI-compatible LLM to score, critique, and guide necessary fixes until the work meets a high, expert-defined standard. It manages state persistence across rounds, simulating the rigorous process of professional peer review.

Behavioral guidelines for writing, reviewing, and refactoring code with LLMs, emphasizing minimalism, clear assumptions, surgical edits, and verifiable success criteria to avoid common machine-assisted coding mistakes.