Skill UI

This system optimizes task execution by recommending the best agent and model tier (Haiku, Sonnet, Opus) based on learned patterns and a 3-tier routing model. It ensures auditable decisions by emitting a detailed routing rationale. Use this before starting any non-trivial task to replace manual selection with data-driven decisions, and record outcomes to continuously improve the router's accuracy.

This tool provides comprehensive configuration for running Large Language Models (LLMs) locally using RuVLLM. It supports initial model setup, specialized fine-tuning via MicroLoRA adapters for domain-specific tasks, and continuous, real-time adaptation using SONA. Use it when transitioning from base models to highly specialized, adaptive AI systems requiring precise parameter control.

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.

A comprehensive skill designed to fetch raw market data (Open, High, Low, Close, Volume) for a given symbol. It normalizes the time series data into relative OHLCV vectors, converts these vectors into 64-dimension embeddings, and stores them in a memory store. Crucially, it indexes the resulting vectors using the HNSW algorithm, enabling fast and precise nearest-neighbor search for pattern detection and similarity analysis in financial time series.

This skill is designed to streamline the process of market research. It helps users gather comprehensive data on target markets, analyze industry trends, and identify key consumer insights. Use it to inform product development, refine business strategies, and make data-driven decisions.

This skill assists in researching prediction markets, treating them as potential data sources or oracle signals for corporate decision intelligence, product development, and agent inputs. It provides source-grounded analysis of implied probabilities, signal quality, and integration patterns, while strictly avoiding investment advice.

A comprehensive guide detailing idiomatic React 18/19 patterns. Covers core principles like component purity, side effect management, hooks discipline, state location decision trees, and advanced topics such as Server/Client Component boundaries (RSC), Suspense, error handling, and using React 19 form actions for building robust, performant, and accessible UIs.

A structured framework for managing complex, iterative, and high-stakes decision processes (e.g., stochastic optimization, ensemble comparison, model rollouts). It mandates the creation of a detailed, auditable 'ledger' that records every prior result, new input, search space metric, and decision mark. This tool enforces rigorous evidence trails and defaults to safe, dry-run modes, ensuring that confidence is built through verifiable, step-by-step proof rather than assumed certainty.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

Standardized expert guidance for return authorization, inspection, grading, disposition decisions, refunds, fraud detection, and vendor recovery in retail and e-commerce operations.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.

This tool detects the current market regime (e.g., bull, bear, ranging, volatile) by leveraging advanced neural network models and technical indicators. It provides a classification, confidence score, and a recommended trading strategy, making it essential for informed decision-making, strategic planning, and enhancing the context before running complex backtests or signals.