Skill UI

Generates production-ready Claude-powered agents with Google’s ADK, bundling React-style loops, tool-augmented capabilities, multi-agent orchestration, workflow automation, and test scaffolding.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

Guides building reliable autonomous agents by covering agent loops, goal decomposition, reflection, and tooling patterns while stressing guardrails, human review, and auditability to curb compounding failure rates in production.

This comprehensive guide serves as a quick reference for diagnosing and fixing the most frequent errors and exceptions encountered when developing with LangChain. It covers issues such as import failures, API authentication problems, structured output parsing errors (Zod), agent looping, and version mismatches, providing root causes and copy-paste solutions for reliable LLM application development.

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

This advanced workflow provides comprehensive pronunciation training by analyzing speech down to the phoneme level. It assesses users' speaking skills, identifies specific weak phonemes (e.g., 'th' vs. 'd'), and runs adaptive drill loops. The system generates detailed weakness reports and recommends targeted practice phrases for continuous accent reduction and fluency improvement.

This protocol defines the structured communication rules for multiple specialized AI agents simulating C-suite roles (e.g., CFO, CTO, CRO). It dictates invocation syntax, response formatting, and enforces strict constraints (such as loop prevention, call depth limits, and isolation rules) to ensure coordinated, non-contradictory, and reliable cross-functional analysis for executive decision-making.

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.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This skill automates the scholarly process of refining a generated academic paper. It executes an iterative loop (Review → Fix → Recompile) using advanced LLMs (like GPT-5.5) over multiple rounds. It is designed not for primary research, but specifically for polishing writing quality, correcting theoretical inconsistencies, improving presentation, and enhancing narrative flow after the initial draft is complete. It helps elevate a paper from a draft to a polished submission.

This skill facilitates autonomous, multi-round research review and refinement. It continuously submits work for assessment via external reviewers (Codex or manual), implements suggested fixes, and re-reviews until the designated positive threshold is met or the maximum number of rounds is reached. It manages state persistence and adjusts difficulty levels for rigorous, iterative quality assurance.