context-engineer

Structure agent context into compact sections with relevance scoring and token budgets.

Updated May 9, 2026

Search Tags:#prompt engineering #agent handoff #token budgeting #llm context #relevance scoring #context packaging #memory organization

Ready to run

npx skills add https://github.com/GustavoGutierrez/engineering-skills --skill context-engineer-gustavogutierrez

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It reduces wasted context length by organizing what matters for the next decision, improving downstream accuracy while lowering token usage.

Core Features & Use Cases

Context triage & relevance scoring: Selects which items are required vs optional vs noise for the current task.
Structured context packaging: Produces compact, downstream-agent-ready context with clear sections (objective, constraints, facts, decisions, dependencies).
Memory and multi-agent handoff rules: Separates stable decisions from transient observations, and defines what each agent should read and defer.

Quick Start

Use the context-engineer skill to build an optimized context package for your next AI task by giving it your goal, constraints, relevant documents, and any prior decisions or open questions.

Frequently Asked Questions about context-engineer

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

FAQPage Schema

How do I reduce LLM context noise and save tokens for better agent decisions?▼

To reduce LLM context noise and save tokens, structure your inputs by separating stable facts from transient observations and applying relevance scoring. This triage process prioritizes required information while discarding noise, producing compact structured context that preserves fidelity for downstream decisions.

What is the best way to package context for multi-agent handoff?▼

The best way to package context for multi-agent handoff is to organize data into clear sections like objective, constraints, facts, and dependencies. This structured context packaging defines what each agent should read versus defer, exposing uncertainty and retrieval boundaries to ensure accurate execution.

How do I organize long chat history for LLM workflows without losing key facts?▼

Organize long chat history for LLM workflows by applying context triage to distinguish required items from optional noise. This memory organization technique separates stable decisions from transient observations, creating a compact context package that maintains fidelity while managing your token budget.

Can I define constraints and retrieval boundaries for prompt workflows?▼

Yes, you can define constraints and retrieval boundaries for prompt workflows by packaging structured context with explicit sections for dependencies and deferred context. This approach exposes uncertainty and separates instructions from facts, ensuring the LLM operates within strict boundaries.

Does context engineering work for coordinating multi-agent execution and token budgeting?▼

Context engineering works for coordinating multi-agent execution and token budgeting by applying relevance scoring to select required versus noise items. It produces downstream-agent-ready context that manages token limits while preserving the fidelity of decisions, dependencies, and constraints across agents.