Boring Engineering

boring-engineering

Build exactly what the current problem requires. No less, no more.

An AI agent skill that turns KISS, YAGNI, and practical DRY into a concrete decision system — so your coding agent stops overengineering by default.

[](./LICENSE) [](https://agentskills.io) [](#setup)

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The Problem

AI agents already know what KISS, YAGNI, and DRY mean. What they consistently fail at is knowing **when** to apply them.

Left alone, agents tend to:

• Add abstractions before there's a second use case
• Build plugin systems for features with one consumer
• Create BaseServiceFactory for a function that needs 10 lines
• Future-proof code for requirements that never arrive

`boring-engineering` fixes this by giving the agent a concrete **4-step decision system** it runs before and during every implementation — not just a reminder to "keep it simple."

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How It Works

The skill loads in three stages (Agent Skills progressive disclosure):

**Stage 1 — Discovery (~100 tokens)** At startup, the agent reads only the `name` and `description` from `SKILL.md` frontmatter. This is how it knows the skill exists without loading the full content.

**Stage 2 — Activation** When you ask the agent to implement, refactor, or review code, it recognises the task matches this skill and loads the full `SKILL.md` body into context.

**Stage 3 — Deep Reference (on demand)** If the agent needs to reason through a complex decision, it loads `references/decision-framework.md` or `assets/decision-tree.md` — only when the instructions point to them. These files never inflate context unnecessarily.

The 4-Step Decision System

Every time the agent writes or modifies code, it runs through:

Step 1 — Requirement Filter (YAGNI)
  Is this explicitly req