context-engineering

Curate project-wide rules, specs, and task-specific sources for agent context.

Updated May 14, 2026

Search Tags:#context management #task management #AI development #developer tool #codebase maintenance

Ready to run

npx skills add https://github.com/soloamente/still --skill context-engineering-soloamente

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-engineering
Source: https://github.com/soloamente/still/tree/main/.cursor/skills/context-engineering
Command: npx skills add https://github.com/soloamente/still --skill context-engineering-soloamente

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps optimize agent context setup to enhance output quality and maintain focus during tasks, preventing issues like hallucinations or losing track of project conventions.

Core Features & Use Cases

Context Management: Manages the structured and selective feeding of information to agents to optimize performance.
Rule and Spec Integration: Incorporates project-wide rules, specs, and architecture documents for context continuity.
Source and Error Handling: Provides source code, test results, and error output for task-specific context.
Use Case: For developers starting a new coding session or dealing with codebase switches, this Skill can be used to configure context effectively and adhere to project standards.

Quick Start

Start the context-engineering skill by loading relevant project context before each coding session or task.

Frequently Asked Questions about context-engineering

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

FAQPage Schema

How do I optimize AI agent context management for better coding performance?▼

AI agent context management is optimized by selectively curating project-wide rules, specs, and task-specific source code to maintain focus and prevent hallucinations during development. This structured feeding of information ensures the agent adheres to project conventions.

Why does my AI agent lose track of project conventions during codebase switches?▼

AI agents lose track of project conventions when context setup lacks structured rule and spec integration. Implementing selective context curation before starting a new coding session ensures architecture documents and project standards are continuously fed to the agent.

What is the best way to configure AI context for a new coding session?▼

The best way to configure AI context for a new coding session is to selectively curate project-wide rules, architecture documents, and task-specific sources like test results before initiating the task. This structured approach prevents the agent from losing focus.

Can I use selective context curation for troubleshooting and error handling tasks?▼

Yes, selective context curation supports troubleshooting by providing task-specific source code, test results, and error output directly into the agent context. This targeted error handling approach optimizes the agent's performance across various development lifecycle stages.

Does context engineering work without external dependencies for codebase maintenance?▼

Context engineering operates with zero external dependencies, relying on internal scripts, references, and assets to manage codebase maintenance. It independently curates the necessary project specs and task-specific sources to maintain AI agent performance.

When should I not use selective context feeding for AI development?▼

Selective context feeding may not be necessary for simple, isolated tasks that do not require adherence to project-wide rules or architecture documents. If a development task lacks complex codebase maintenance or spec integration, structured context management offers limited benefit.