Context engineering

Skill sohaibdevv/AI-AGENT-SKILLS/skills/context-engineering

Manage what goes into the AI agent context window for maximum quality and minimum wasteFrom its SKILL.md

Install

npx -y skills add sohaibdevv/AI-AGENT-SKILLS --skill context-engineering

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.4 KB, 473 tokens by cl100k_base, as published. Nobody here has run it

Overview

Context is finite. What you put in the context window determines what the agent can reason about. Too much noise → the relevant signal is diluted. Too little context → the agent makes uninformed decisions. This skill manages context deliberately.

When to Use

When an agent produces low-quality outputs despite correct instructions
When designing a system prompt for a production agent
When a long conversation is causing quality degradation
When context costs are higher than expected

Process

Step 1: Define the context budget

For your model and use case: how many tokens is your budget? Reserve: 20% for the system prompt, 20% for the output, 60% for the dynamic context (documents, history, tools).

Step 2: Prioritize context by relevance

Include in this order:

Task instructions (always)
The most relevant documents or code (retrieved, not full codebase)
Relevant conversation history (not all history)
Supporting context (schemas, type definitions)

Cut: long documents that contain 1 relevant paragraph, full file contents when only a function is needed, conversation history beyond the last N relevant turns.

Step 3: Structure context for retrieval

Agents pay more attention to the beginning and end of context. Put instructions at the top. Put the most relevant context closest to the task.

Step 4: Use explicit context delimiters

Mark different sections clearly:

<system>Your role and constraints</system>
<documents>Retrieved content</documents>
<task>What to do</task>

This prevents the model from confusing instructions with retrieved data.

Step 5: Compress context aggressively

Summarize long histories. Extract the relevant portions of long documents. Use structured data (JSON, tables) instead of prose where possible.

Step 6: Monitor context quality

Track: output quality vs. context length. If longer context is producing worse results, you have a context quality problem, not a context quantity problem.

Verification Requirements

Context budget defined
Context prioritized: instructions → relevant docs → history
Long content compressed or chunked
Context sections delimited clearly
Output quality monitored relative to context composition