Skill UI

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.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

Performs multi-baseline counterfactual cost analysis by comparing actual session spending against hypothetical costs generated by fixed, non-optimal models (e.g., always-haiku, always-sonnet). This tool quantifies the financial savings realized by the smart routing mechanism, answering the critical question: 'Is the routing earning its keep?' It is ideal for quarterly cost reviews, setting CI performance gates, and validating routing configurations.

This tool generates a detailed cost report, tracking token usage and associated USD costs across all AI agents and models. It provides crucial insights into budget utilization, breaking down spending by model (e.g., Opus, Sonnet), agent, and operational tier (Tier 1/2/3), helping teams manage spending and optimize resource allocation.

This tool automatically reads usage data from active AI coding sessions (JSONL format). It tallies token usage per model and tier, calculates the total cost in USD using specified pricing models, and persists a structured record into the cost-tracking namespace. Use this after completing a chunk of work or at the end of a session to provide accurate data for cost reporting and optimization.

This skill analyzes and reports on the token usage, expenditure, and budget for AI models (like Claude Code) by querying a local SQLite database. It is essential for users who need detailed financial accountability, tracking costs by project, tool, or date, and managing operational budgets.

A comprehensive guide to implementing Test-Driven Development (TDD) for Django projects. This skill covers the full Red-Green-Refactor cycle, utilizing pytest, pytest-django, and factory_boy to write robust unit and integration tests for Django models, views, and DRF APIs. Essential for developers aiming to build large-scale, high-quality Python applications.

A comprehensive skill for generating multimodal media content using fal.ai models. It supports text-to-image (Nano Banana), text/image-to-video (Veo, Kling, Seedance), and text/video-to-audio (CSM-1B, ThinkSound). Use this tool for any creative project requiring high-quality generated images, videos, or sound effects.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

A comprehensive framework for optimizing inventory levels and planning replenishment in multi-location retail environments. It covers advanced demand forecasting techniques (e.g., Holt-Winters, ML models), calculating safety stock based on variability, estimating promotional uplift, and generating actionable purchase orders while minimizing stockouts and excess inventory. Ideal for professionals managing complex SKU portfolios across various supply chain systems.

A comprehensive guide for creating a consistent, credible, and highly defensible suite of investor-facing materials. This tool ensures alignment across pitch decks, financial models, one-pagers, and memos by enforcing a single source of truth for all metrics, assumptions, and use of funds, significantly boosting fundraising credibility.

Provides comprehensive configuration management for local LLM inference using RuVLLM. Users can select models, perform task-specific fine-tuning with MicroLoRA adapters, or implement continuous, real-time adaptation using SONA. This tool guides the entire advanced LLM workflow, covering status checks, configuration generation, and advanced adaptation loops.