Skill UI

This skill guides users through complex, multi-step processes such as installation, configuration, or initial setup. It breaks down the process into small, atomic actions, presenting only the current required step and a limited, manageable checklist of remaining tasks. Ideal for onboarding new users or executing lengthy technical setups.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

Adds x402 payment execution for AI agents with task budgets, spending controls, and non-custodial wallets. Supports Base via agentwallet-sdk and X Layer via OKX payment protocols for controlled API and service payments.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.

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.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

This function executes a full, autonomous iteration of the Claude Code workflow. It systematically checks the current status, predicts the optimal next action (e.g., editing code, running tests, spawning agents), executes the task, logs the results, and schedules the next wakeup. It discovers and processes tasks from team lists, swarm lists, and file checklists, ensuring continuous and automated progress toward a defined goal.

This skill utilizes learned patterns and the current state to predict the most optimal next action in a complex, multi-step workflow. It guides the execution flow by checking prediction confidence: high confidence leads to immediate execution, while low confidence triggers a fallback to task list prioritization. Crucially, it continuously learns from completed tasks to update patterns, ensuring improved, self-correcting automation across sessions.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

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 wraps the hooks routing mechanism to classify tasks into tiers based on Agent Booster eligibility. It calculates potential cost savings by identifying Tier 1 bypass tasks (which cost $0) versus standard LLM calls. It is essential for optimizing large batches of simple, predictable operations and reporting accurate resource utilization.

This tool automatically captures and calculates the token usage and associated USD cost from an active Claude Code session JSONL file. It structures this detailed record, including per-model and per-tier breakdowns, and persists it to the `cost-tracking` namespace. Use this producer step after completing a major task or when ending a session to ensure accurate cost reporting for subsequent analysis.