Context Fundamentals
This is the foundational reference for understanding how language models actually use context: system prompts, tool definitions, retrieved documents, message history, and tool outputs. The core insight is treating context as a finite attention budget rather than just a token limit. It explains why throwing more context at a problem often makes things worse, covers the progressive disclosure pattern for loading information only when needed, and walks through practical tradeoffs like tool outputs consuming 80%+ of tokens in typical agent runs. Use this when designing agent architectures, debugging weird behavior, or explaining to your team why that 200k context window won't magically solve your memory problems. The section on prompt altitude is especially good.
- skills.sh installs
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- last updated
- Jun 3, 2026
Install
$ npx -y skills add sickn33/antigravity-awesome-skills --skill context-fundamentals --agent claude-codeInstalls into .claude/skills of the current project.
Files
Context Engineering Fundamentals
Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.
When to Use
Activate this skill when:
- Designing new agent systems or modifying existing architectures
- Debugging unexpected agent behavior that may relate to context
- Optimizing context usage to reduce token costs or improve performance
- Onboarding new team members to context engineering concepts
- Reviewing context-related design decisions
Core Concepts
Context comprises several distinct components, each with different characteristics and constraints. The attention mechanism creates a finite budget that constrains effective context usage. Progressive disclosure manages this constraint by loading information only as needed. The engineering discipline is curating the smallest high-signal token set that achieves desired outcomes.
Detailed Topics
The Anatomy of Context
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- Repository
- github.com/sickn33/antigravity-awesome-skills
- First seen
- Jun 3, 2026
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