context-engineering-fundamentals
FeaturedManages attention and evidence in long agent sessions. Use for lost instructions, dropped evidence, or large multi-agent contexts.
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Quality Score: 89/100
Skill Content
Details
- Author
- jamditis
- Repository
- jamditis/claude-skills-journalism
- Created
- 7 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- MIT
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
context-engineering
Use when managing what an LLM sees. Covers context-window budgeting, retrieval and compaction, memory across turns, tool-result pruning, and the failure modes that come from too much context rather than too little.
context-fundamentals
Explain or reason about foundational context engineering concepts: what context is, the anatomy of a context window, attention mechanics, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret context-engineering decisions. Use for conceptual explanation, onboarding, and background reading. Route operational work to context-degradation for attention failures and context-optimization for token-efficiency work.
context-engineering
Engineer what goes into the LLM context window — system prompts, retrieved docs, tool schemas, conversation history, memory, examples. Apply the four operations write/select/compress/isolate to manage context as a finite resource. Enforce the 40% rule on context utilization. Use whenever the user is designing system prompts, debugging quality degradation in long conversations, hitting context limits, managing per-step retrieval, dealing with sub-agent context isolation, or asking about "context engineering" / "prompt engineering" / CLAUDE.md / AGENTS.md / instruction files.