context-fundamentals
SolidContext 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.
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Quality Score: 96/100
Skill Content
Details
- Author
- sickn33
- Repository
- sickn33/antigravity-awesome-skills
- Created
- 4 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
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.
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.
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.
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).
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`).