context-engineering

Configures rules files and curates context layers to improve AI agent output quality.

5|Updated Mar 5, 2024
One-click install
npx skills add https://github.com/TRAPZZY/God-Eyes --skill context-engineering-trapzzy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-engineering
Source: https://github.com/TRAPZZY/God-Eyes/tree/main/.skills/context-engineering
Command: npx skills add https://github.com/TRAPZZY/God-Eyes --skill context-engineering-trapzzy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI coding agents produce inconsistent or hallucinated output when they lack the right project context, and they lose focus when overloaded with irrelevant information. This Skill provides a structured methodology for curating what the agent sees, when it sees it, and how it is structured. ## Core Features & Use Cases - Context Hierarchy Framework: Organizes context into five levels from persistent rules files (CLAUDE.md, .cursorrules, AGENTS.md) down to transient conversation history, with templates for each level. - Context Packing Strategies: Provides the Brain Dump, Selective Include, and Hierarchical Summary patterns for loading task-relevant files, specs, and examples without flooding the context window. - Confusion Management: Defines explicit patterns for surfacing conflicting specs, missing requirements, and ambiguity instead of silently guessing, plus an inline planning pattern for multi-step tasks. - Use Case: When starting a new project for AI-assisted development, use this Skill to create a CLAUDE.md rules file covering tech stack, commands, conventions, and boundaries so every agent session follows project standards. ## Quick Start Ask the agent to create a CLAUDE.md rules file for your project covering the tech stack, build commands, code conventions, and boundaries before starting the next coding session.

Frequently Asked Questions about context-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I improve AI coding agent output quality?▼

Create a rules file such as CLAUDE.md covering your tech stack, commands, code conventions, and boundaries, then load only task-relevant source files before each task. Persistent rules files are the highest-leverage context you can provide.

What should a CLAUDE.md file contain?▼

A CLAUDE.md file should include the project name, tech stack, build and test commands, code conventions, boundaries such as never committing secrets, and one short example of a well-written component in your style.

How much context should I give an AI agent per task?▼

Aim for under 2,000 lines of focused context per task. Loading more than 5,000 lines of non-task-specific content causes the agent to lose focus, since context window size does not equal attention budget.

Why does my AI agent ignore project conventions?▼

Agents ignore conventions when no rules file exists or when conventions are only implicit knowledge. If a rule is not written down in a persistent file like CLAUDE.md or .cursorrules, the agent cannot follow it.

When should I start a fresh AI coding session?▼

Start a fresh session when switching between major features or when agent quality degrades as the conversation grows longer. Long conversations accumulate stale context that causes the agent to reference outdated patterns or deleted code.

What are the limitations of large context windows for agents?▼

Large context windows do not guarantee better output because attention degrades with too many instructions. Selective, focused context consistently outperforms dumping entire specs or codebases into the prompt.