Skill UI

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

Use this tool to query, filter, and compare over 400 available AI models from OpenRouter. It allows users to check pricing, context lengths, model capabilities (e.g., tool calling), and select the best model based on specific needs, ensuring optimal performance and cost efficiency in AI development.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

This skill provides a comprehensive guide to understanding OpenRouter's API billing structure. It teaches users how to accurately calculate costs based on prompt (input) and completion (output) tokens. Users can utilize provided scripts to estimate costs, check credit balances, and implement cost-saving strategies like using `:floor` or `:free` model variants. Ideal for budgeting, performance comparison, and production cost tracking.

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.

Expert skill for writing, reviewing, and debugging idiomatic Rust code, focusing on memory safety and zero-cost abstractions. Specializes in advanced concepts like ownership patterns, explicit lifetime management, trait-based API design, and building concurrent async applications using Tokio. Use this when developing high-performance, reliable, systems-level software in Rust.

Build comprehensive 3-5 year financial models essential for startup planning and fundraising. This skill meticulously projects revenue using cohort-based methods, details cost structures (COGS, S&M, R&D, G&A), plans headcount, and performs detailed cash flow analysis. It calculates critical metrics like LTV, CAC, and burn rate, providing three scenarios (conservative, base, optimistic) for robust strategic decision-making.

Build comprehensive 3-5 year financial models crucial for early-stage startups. This guide covers revenue projections using cohort-based methods, detailed cost structuring (COGS, S&M, R&D, G&A), robust cash flow analysis, and multi-scenario planning (Conservative, Base, Optimistic). It provides the quantitative foundation needed for fundraising, strategic planning, and investor presentations.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.

This comprehensive guide details the credit-based usage model of the Windsurf AI platform. It explains how various powerful models (e.g., SWE-1, GPT-4o) consume credits and provides strategies for optimizing AI usage costs. Learn how to monitor remaining credits, understand different pricing tiers (Free, Pro, Teams), and implement prompt engineering techniques to maximize efficiency and prevent mid-session interruptions.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Guides entrepreneurs through minimalist pricing, helping them choose cost-based or value-based models, set an initial price, plan pricing tiers, and calculate how many customers they need to reach financial independence.