Skill UI

This skill automates the identification and removal of unused AWS resources such as EBS volumes, snapshots, and Elastic IPs to reduce cloud costs. It includes scripts for dry-run validation, cost impact calculation, and Lambda-based automation workflows. Users can safely clean up storage, compute, and networking waste while ensuring dependency checks and rollback plans are in place.

This skill guides you through a comprehensive, multi-phase interview to capture every necessary detail of a new project idea. It is designed to run BEFORE any code is written, forcing detailed specification gathering across purpose, features, data models, and technical constraints. The output is a clean, self-contained prompt.md file, ensuring subsequent development is based on solid, unambiguous requirements and preventing costly mistakes.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

This skill helps design marketing messages using loss aversion psychology. It compares gain and loss framing to emphasize credible costs of inaction without fear-mongering. Use it for copy, UX, email, or pricing to create urgency based on audience risk tolerance and trust levels. It ensures ethical persuasion by avoiding fake threats and maintaining user autonomy.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

Create comprehensive 3-5 year financial models for early-stage startups, including revenue projections, cost structures, cash flow analysis, and scenario planning. Supports fundraising, operational strategy, and investor presentations with cohort-based revenue modeling and headcount planning.

A quota-aware parallel subagent coordination skill for Antigravity 2.0. Decomposes large multi-file tasks into isolated agent missions, manages model selection based on cost, and ensures efficient parallel execution while monitoring quota usage. Ideal for complex projects spanning multiple files and components.

This skill automatically routes subtasks to the most cost-effective Claude model (Haiku, Sonnet, or Opus) based on complexity. It logs real token usage and cost per session, and includes an A/B testing feature to validate model tier savings against your actual workload, ensuring accurate cost control.

Calculate the cost per million tokens for running AI models locally on your own hardware like Ollama or llama.cpp. It factors in electricity prices, hardware costs, power consumption, and generation speed to determine the break-even point against hosted API models. Useful for deciding whether to deploy models locally or use cloud services based on actual usage and financial constraints.