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name: engineering-claude-context description: Curates context, optimizes prompts with XML, and manages extended thinking for Anthropic Claude models. Use when building Claude-based agents, designing system prompts, or handling long-context tasks.

Claude Context Engineering

Instructions

Follow these strategies to maximize reliability and performance for Anthropic Claude models within limited attention budgets.

1. Curate High-Signal Context

   • Treat context as a finite resource; every token dilutes attention.
   • Prune low-value information (e.g., repetitive logs, unused tool outputs).
   • Use "Just-in-Time" Retrieval: Provide lightweight identifiers (file lists, summaries) initially, allowing Claude to load full details only when needed.

2. Structure with XML Tags

   • Use XML tags to strictly delineate prompt sections (Claude is optimized for this).
   • Common tags: <instructions>, <context>, <tools>, <examples>, <formatting>.
   • Nest tags for hierarchy (e.g., <examples><example>...</example></examples>).
   • Reference tags explicitly in instructions (e.g., "Analyze the data in <context>").

3. Optimize for Long Horizons

   • Compaction: Periodically summarize conversation history to free up context while preserving key state (decisions, bugs).
   • Memory: Implement an external "notebook" (e.g., scratchpad.md) where Claude reads/writes persistent state, freeing it from the context window.
   • Sub-Agents: Delegate distinct, context-heavy sub-tasks to ephemeral sub-agents that return only distilled results.

4. Leverage Extended Thinking

   • Use for: Complex STEM problems, constraint optimization, and multi-step strategic frameworks.
   • Prompting: Use open-ended, high-level instructions ("Think thoroughly about X") rather than rigid step-by-step constraints for reasoning.
   • Few-Shot: Include <thinking> blocks in your examples to demonstrate the reasoning process, not just the final output.
   • Budget: Start small (1024 tokens) and scale up for complexity (up to 32k+ with batch processing).

5. Design Token-Efficient Tools

   • Ensure tools return only necessary data (e.g., grep output vs. full file).
   • Keep tool definitions clear and distinct to avoid ambiguity.

Critical Rules

• No Brittle Logic: Avoid hardcoding complex if/else chains in prompts; use examples ("pictures") to guide behavior instead.
• Don't Prefill Thinking: Never prefill Claude's response when using Extended Thinking mode.
• Progressive Disclosure: Don't dump all data at once. Let Claude discover information layer by layer (hierarchy -> summary -> detail).
• Clear Separation: Always separate System Instructions from User Data to prevent prompt injection and confusion.

Checklist

• Tags: Are distinct sections wrapped in XML tags?
• Efficiency: Is the initial context free of unnecessary bulk?
• Retrieval: Does Claude have tools to fetch details "just-in-time"?
• Memory: Is there a mechanism (compaction or external file) for long-term persistence?
• Examples: Do few-shot examples demonstrate the reasoning (if using thinking) or format required?
• Thinking: Is Extended Thinking enabled only for complex/planning tasks, not simple lookups?

Detailed Guidance

For deep dives on context anatomy, compaction strategies, and extended thinking patterns, see REFERENCE.md.