Skill UI

This skill performs expert appraisal of properties involved in auctions. It calculates market value, forced liquidation prices, and applies Brazilian standards like ABNT NBR 14653. It uses comparative, income, and cost methods, along with CUB and safety margin calculations. Suitable for legal and financial analysis of auctioned real estate.

Expert prompt engineering skill that transforms basic instructions into production-ready LLM prompts using constitutional AI, chain-of-thought reasoning and model-specific optimization to boost accuracy and cut token costs.

Cut LLM costs and latency with three caching layers: exact-match Redis cache, semantic similarity cache (GPTCache/Qdrant), and provider-side prompt caching. Ideal for FAQ bots, support tools and high-volume APIs.

This skill helps reduce LLM API and infrastructure costs by 50-90% using strategies like model selection, prompt caching, batching, quantization, and self-hosting. It covers cost tracking, right-sizing models for tasks, provider-side caching, and semantic caching to avoid redundant calls. Suitable for developers managing AI spend and optimizing cloud infrastructure.

Deploy a unified API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.

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.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

This skill analyzes data warehouses to identify stale, unused, or redundant tables that increase storage costs. It classifies waste patterns, computes safety tiers, and supports category drill-downs. Compatible with Snowflake, BigQuery, Redshift, and Databricks. Use it when users ask about storage optimization, cost reduction, or cleanup opportunities.

This skill provides a comprehensive decision framework and set of patterns for architects designing cloud-agnostic applications across major providers like AWS, Azure, and GCP. It details service comparisons (Compute, Storage, DB), multi-cloud patterns (e.g., Best-of-Breed, DR), cost optimization strategies, and structured migration plans, ensuring robust and resilient enterprise deployments.

Diagnose and fix excessive Postgres egress (network data transfer) in a codebase. Use when users report high database bills, unexpected data transfer costs, egress spikes, or queries fetching more data than needed. Provides SQL diagnostics via pg_stat_statements and code-level fixes for common anti-patterns like SELECT *, missing pagination, and high-frequency static queries.

Nika is an open-source Rust engine that captures repeatable AI tasks as checkable YAML workflow files. It audits workflows before token spending, enforces strict cost budgets, and executes against local or cloud providers. Ideal for daily digests, ETL, and multi-step pipelines requiring audit trails and deterministic execution without autonomous coding capabilities.

This skill covers configuring and managing object storage solutions like AWS S3, MinIO, and GCS. It includes CLI operations for bucket management, uploading/downloading files, setting up versioning, implementing bucket policies for access control, and configuring lifecycle rules for cost optimization. Suitable for DevOps engineers managing application assets, backups, logs, and media files in cloud or on-premises environments.