context-fundamentals

Solid

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.

AI & Automation 39,227 stars 6374 forks Updated today MIT

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Skill Content

# 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 **System Prompts** System prompts establish the agent's core identity, constraints, and behavioral guidelines. They are loaded once at session start and typically persist throughout the conversation. System prompts should be extremely clear and use simple, direct language at the right altitude for the agent. The right altitude ...

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Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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context-fundamentals

This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.

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This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.

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context-fundamentals

This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.

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Kalyanikhandare29
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context-fundamentals

Foundational theory of context engineering — what context IS, how attention works, progressive disclosure principles, and context budgeting basics. Use when the user asks to "understand context", "explain context windows", "learn context engineering", or discusses context components, attention mechanics, or context budgets. NOT for fixing broken context or diagnosing failures (use context-degradation), NOT for compressing or summarizing context (use context-compression), NOT for KV-cache or partitioning performance optimization (use context-optimization), NOT for file-based context patterns or scratch pads (use filesystem-context).

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viktorbezdek
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context-engineering

Use when designing what information reaches an LLM agent before it reasons — system prompt, persistent memory, always-loaded rules, injected skills, and the user prompt — or when diagnosing why an agent produced a wrong answer despite a clear instruction. Covers the four context failure modes (missing, stale, wrong, overwhelming), the five-layer context stack, four context quality metrics (injection precision and recall, utilization, freshness), the Frequent Intentional Compaction (FIC) protocol, subagent delegation for context-heavy work, and the failure-mode decision tree. Do NOT use for prompt wording (use `prompt-craft`), authoring a new SKILL.md (use `skill-scaffold`), or deciding which skill the router activates for a given query (use `skill-router`).

0 Updated 5 days ago
jacob-balslev