Skill UI

This powerful skill leverages learned patterns and the current operational state to predict the most efficient next action. It guides complex workflows by offering immediate, high-confidence predictions. If confidence is low, it intelligently falls back to task priority lists. After every completed task, it utilizes a robust learning loop to update success patterns, ensuring continuous process improvement and cross-session memory recall.

This skill provides a rigorous, measured framework for optimizing system performance. It guides users to convert vague goals (e.g., 'make it faster') into a bounded, repeatable process. The loop requires establishing a clear baseline, systematically testing hypotheses (variants), identifying bottlenecks, and promoting the safest, most measurably improved solution, ensuring reproducibility and stability throughout the process.

This tool enables complex web interactions using plain natural language commands. Instead of relying on unreliable selectors, it utilizes a powerful page-agent and LLM loop to interpret user intent, performing actions like filling forms, clicking buttons, and extracting data from dynamic websites. Ideal for testing and automation in pages with ambiguous or changing markup.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

Monitors the acceleration trend of production spending (burn rate) over defined time windows. It compares the latest spending bucket against the historical average, providing an early warning system. This is crucial for detecting sudden, unexpected cost spikes, such as a 'hot loop,' independent of overall budget limits.

This utility manages scheduled, persistent background workers using the `CronCreate` mechanism. Unlike simple in-session loops, scheduled cron jobs survive system restarts, making them ideal for Continuous Integration (CI), system monitoring, and scheduled maintenance tasks (e.g., security audits, performance optimization, data consolidation). Users can define various recurring schedules and understand the difference between persistent cron scheduling and active session looping.

This guide outlines Dynamic Workflow Mode, enabling coding agents to generate structured, task-local harnesses for complex, multi-step processes. It establishes a disciplined system for temporary workarounds, extracting reusable shared skills, and implementing control pane checkpoints. Use this when tasks require custom loops, multi-agent collaboration, or reliable handoff artifacts.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

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.

This skill guides the design and rigorous review of goal-oriented agent feedback loops. It helps ensure that complex autonomous multi-step agents are robust, preventing failures such as infinite looping, goal-gaming, or running to completion with incorrect assumptions. It covers defining machine-decidable goals, setting boundaries, and selecting the correct loop type (servo, regulator).