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Ai context engineering

Skill ebarti/skills/ai-context-engineering

Foundational context engineering theory and practice for LLM applications. Covers the 5 levels of context (zero/linear/goal-oriented/role-based/semantic blueprint), semantic role labeling (SRL) for visualizing structured prompts, and the layered (scope → investigation → action) analysis pattern. Use this skill when: - Choosing between prompt engineering and context engineering for a task - Designing context that includes role, goals, and structured intent - Building a meeting / document / interview analysis pipeline - Visualizing the semantic structure of a prompt with SRL - Deciding when to upgrade a prompt's "context level"From its SKILL.md

Install
npx -y skills add ebarti/skills --skill ai-context-engineering

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SKILL.md

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AI Context Engineering

Knowledge from "Context Engineering for Multi-Agent Systems" (Chapter 1). Foundational theory for moving from prompts to engineered context.

Quick Start

  1. Check guidelines.md to find which files to load
  2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
  3. Apply guidance to your work

Contents

References

CategoryPurpose
semantic-blueprintThe 5 levels of context, semantic blueprint definition, SRL theory
srl-implementationPython implementation of SRL with matplotlib visualization
meeting-analysisThe 3-layer (scope/investigation/action) analysis pattern with worked example

Workflows

WorkflowPurpose
workflows/build-3-layer-pipeline.mdEnd-to-end build of a scope→investigation→action analysis pipeline

Guidelines

See guidelines.md for task-based file selection.

What ships with it: 15 files

70.3 KB alongside SKILL.md

references/

workflows/

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