Skill UI

A robust reference architecture designed for integrating multi-property portfolios with the AppFolio Stack API. It manages complex tasks such as real-time vacancy tracking, tenant lifecycle management, work order routing, and financial accounting reconciliation (e.g., QuickBooks/Xero). Key components include a service layer, multi-level caching strategies for optimal data freshness, and an event-driven pipeline (using webhooks and message queues) to ensure data consistency and idempotency across all transactions, addressing potential rate limits and failure modes.

This meta-skill acts as a routing layer for complex UI development tasks. It guides the process of selecting the smallest, most specific, and useful skill context using the ui-skills CLI. Use it when starting UI work to analyze goals, identify the correct category, and pinpoint the precise skill set needed for implementation, ensuring development efficiency and accuracy.

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 pattern for building robust and cost-efficient LLM application pipelines. This guide details combining model routing based on task complexity, immutable cost tracking, limited retry logic, and prompt caching to manage API expenditures while maintaining high quality. Essential for production systems handling variable-complexity, high-volume tasks.

This tool routes tasks through a multi-tier system, specifically partitioning tasks into Agent Booster eligible (Tier 1) and standard LLM calls (Tier 2/3). It calculates and reports the estimated cost, leveraging the structural $0 cost of Booster bypasses for simple transforms, helping users optimize spend before running batches of similar tasks.

A sophisticated system designed to automatically select the optimal AI agent and model tier (e.g., Haiku, Sonnet, Opus) for any non-trivial task. It replaces manual decision-making by integrating learned patterns, predictive modeling, and a robust 3-tier routing mechanism. The process is fully auditable, providing a detailed routing rationale to ensure transparency and reliable execution.

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.