tanaikech

A Developer's Guide to Understanding Agent Skills

Subtitle: Implementing Progressive Disclosure in Google Apps Script

Motivation for Writing

As an active researcher and developer in the AI ecosystem, I have seamlessly integrated Agent Skills into daily workflows using tools like Claude Code, Gemini CLI, and Antigravity. However, I observed a pervasive tendency in the developer community—and initially within my own practice—to treat these capabilities as opaque black boxes. There is a distinct lack of granular understanding regarding the internal execution steps and the recursive orchestration occurring within Generative AI models when a skill mandates subagent delegation.

To bridge this critical knowledge gap, I conducted a rigorous investigation into the architecture of Agent Skills. Documenting this paradigm shift from rudimentary "tools" to sophisticated "multi-agent workflows" will not only formalize these mechanisms but also provide vital insights for developers constructing scalable, enter

tanaikech

Agent-as-a-Tool: A New Era of AI Orchestration

Abstract

As Large Language Model (LLM) agents increasingly integrate numerous external systems, they suffer from Tool Space Interference (TSI), a phenomenon causing context bloat, attention dilution, and degraded reasoning accuracy. In this paper, we introduce the Agent-as-a-Tool paradigm—an evolutionary, practical implementation of the recently proposed Self-Optimizing Tool Caching Network (SOTCN) and Federated Context-Aware Routing Architecture (Federated CARA). By leveraging Retrieval-Augmented Generation (RAG) to dynamically discover and assemble stateful, autonomous sub-agents on the fly, this architecture completely eliminates TSI, enforces Zero-Trust execution boundaries, and achieves infinitely scalable AI orchestration.

Introduction

tanaikech

Next-Generation Google Workspace Automation

A Comparative Study of Agentic Frameworks and Multi-Agent Orchestration

Abstract

The transition from passive chatbots to autonomous execution environments was cemented at Google Cloud Next '26 with the introduction of the Gemini Enterprise Agent Platform. This paper evaluates four cutting-edge AI agent methodologies for Google Workspace automation, developed by leading developers Martin Hawksey, Bruce Mcpherson, and Kanshi Tanaike. We deconstruct their structural approaches—CLI skill chaining, advanced emulation sandboxing, dynamic code generation, and A2A remote delegation—demonstrating how these community-driven innovations anticipated native Next '26 features like the official Agent Skills repository and Model Context Protocol (MCP) support. Building upon these foundations, we propose two novel frameworks: the Federated Context-Aware Routing Architecture (Federated CARA) for zero-trust, multi-cloud task routing, and the Self-Optimizing Tool Caching Ne

tanaikech

Empowering Autonomous AI Agents through Dynamic Tool Creation

Abstract

Welcome to the Agentic Enterprise era. This article explores a paradigm shift in generative AI workflows by introducing an autonomous agent capable of dynamically creating, testing, and executing original tools. Utilizing Google Apps Script, Node.js emulation, and multi-agent orchestration, this architecture overcomes traditional limitations, enabling highly adaptable task execution.

Introduction

tanaikech

Orchestrating Agents via ADK for TypeScript and Gemini CLI

Abstract

Explore how to build and orchestrate production-ready, type-safe AI agents using Google's TypeScript Agent Development Kit (ADK). This guide provides practical scaffolding patterns, multi-agent coordination strategies, and seamless integration techniques for deploying remote subagents within the Gemini CLI ecosystem.

Introduction

tanaikech

Integrating Remote Subagents Built by Google Apps Script with Gemini CLI

Abstract

This article explores integrating remote subagents built with Google Apps Script into the Gemini CLI using the Agent-to-Agent (A2A) protocol. It demonstrates how bypassing standard authentication via local agent cards enables seamless execution of complex workflows while effectively overcoming Tool Space Interference (TSI) for massive toolsets.

Introduction

tanaikech

Bypassing Installable Triggers: Monitoring Sheet Changes with New SHEET and SHEETS Functions

Abstract

Google Sheets recently introduced the SHEET and SHEETS functions. Because they automatically recalculate upon structural changes, developers can utilize them as custom triggers. This article demonstrates how to leverage these functions to detect sheet insertions, deletions, renames, and movements without requiring cumbersome installable triggers in Google Apps Script.

Introduction

On February 23, 2026, Google introduced two pivotal built-in functions to Google Sheets: SHEET and SHEETS Ref. The SHEET function returns the index (sheet number) of a specified sheet or reference Ref. Meanwhile, the SHEETS function provides the total count of sheets within a spreadsheet Ref.

tanaikech

Recursive Knowledge Crystallization: Enabling Persistent Evolution and Zero-Shot Transfer in AI Agents

Abstract

This paper presents a self-evolving framework, Recursive Knowledge Crystallization (RKC), designed to overcome the "Catastrophic Forgetting" inherent in autonomous AI agents. By persisting evolved technical insights into a universally readable SKILL.md file based on the Agent skills specification, this approach establishes long-term memory and cross-platform portability. The framework was empirically validated through the development of gas-fakes, a highly complex Node.js-to-Google Apps Script (GAS) emulation library. The results demonstrate that agents can autonomously internalize project-specific architectural patterns and environmental nuances. Consequently, the framework achieves Zero-Shot Knowledge Transfer across distinct toolcha

tanaikech

Mastering Google Apps Script CI/CD: Seamless GitHub Actions Integration with gas-fakes

Abstract

Discover how to seamlessly integrate Google Workspace with GitHub Actions using the gas-fakes library. This guide demonstrates running Google Apps Script locally and within CI/CD pipelines without deploying Web Apps. Automate workflows, secure credentials, and effortlessly interact with Google Drive and Sheets directly from your repository.

Introduction

tanaikech

Recursive Knowledge Crystallization: A Framework for Persistent Autonomous Agent Self-Evolution

Abstract

In the development of autonomous agents using Large Language Models (LLMs), restrictions such as context window limits and session fragmentation pose significant barriers to the long-term accumulation of knowledge. This study proposes a "self-evolving framework" where an agent continuously records and refines its operational guidelines and technical knowledge—referred to as its SKILL—directly onto a local filesystem in a universally readable format (Markdown). By conducting experiments across two distinct environments featuring opaque constraints and complex legacy server rules using Google's Antigravity and Gemini CLI, we demonstrate the efficacy of this framework. Our findings reveal that the agent effectively evolves its SKILL through iterative cycles of trial and error, ultimately saturating its learning. Furthermore, by tr