TECH BLOG
Tech Blog
Original articles on AI and programming, in Chinese and English.
After Writing Dozens of Skills, I Distilled This Engineering Method: From `Never Triggered` to `Production-Ready`
An engineering method for writing Skills that are correct, stable, and maintainable long-term, distilled from writing dozens of them. Skills are three layers, not one file: L1 metadata for routing, L2 body for behavior, L3 resources for facts and determinism. Most "never triggers" are routing failures — fixed by a `description` that says what, when, and especially when *not* to use it. Execution drift is fixed with numbered steps, stop conditions, and rule-plus-reason instructions. Guardrails turn "should" into machine-checkable checkpoints, with tiered failure handling. Splitting should follow change-reasons, not file length. Four eval metrics (routing, step-following, checkpoints, task success) pin down what to fix. Ends with versioning, a trace-reading workflow, and a copy-paste checklist.
From Agent Loop to PlanMode: A Production-Ready AI Agent Engineering Skeleton
A production-ready AI Agent engineering skeleton explained through an operating-system analogy. The Harness is the OS, the LLM is the CPU, and the Agent Loop is the kernel's scheduling loop — constantly assembling context, dispatching tool calls, and waiting for I/O. A clear termination condition, real verification, and disk persistence are non-negotiable. Two-Stage ReAct separates thinking from doing: a dry-run first, then real execution. Four tools — read, write, edit, bash — map to file-system operations, scheduled in parallel for read-only and serially for writes. AGENTS.md, Skills, and PlanMode form a three-level memory system that keeps long-running agents on track. Includes ready-to-use pseudocode.
One Line of Code That Can 10× Your Agent Costs: KV-Cache-Friendly Agent Design
Agents running slow and expensive? The problem often lies in a broken KV Cache. This article starts with the fundamentals of KV Cache and lays out three golden rules: keep system prompts and tool definitions byte-level fixed, always append dynamic information to the end, and use standard API formats instead of hand-crafting your own. Using Qwen3, DeepSeek, and Claude as examples, it analyzes how different models differ in chain-of-thought feedback and what cache killers to watch out for, helping you build cache observability so that both first-token latency and your bill go down.
Welcome to the Yatu Tech Blog
New tech blog section with articles on AI, programming and dev tools. This post demonstrates code highlighting and typography.