Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
Provides prompt engineering standards and context optimization techniques based on Anthropic best practices. Use when you need help structuring prompts, managing context windows, or designing AI agents with clear, high-signal instructions.
/plugin marketplace add otrebu/agents/plugin install meta-work@otrebu-dev-toolsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
workflows/create-prompt.mdworkflows/optimize-prompt.mdContext engineering = Curating optimal set of tokens during LLM inference
Primary Goal: Find smallest possible set of high-signal tokens that maximize desired outcomes
Use clear semantic sections:
✅ Good: "Validate input before processing" ❌ Bad: "You should always make sure to validate..."
✅ Good: "Use calculate_tax tool with amount and jurisdiction" ❌ Bad: "You might want to consider using..."
✅ Good: Bulleted constraints ❌ Bad: Paragraph of requirements
Don't load full data dumps - use references and load when needed
Persist important info outside context window
Delegate subtasks to specialized agents with minimal context
❌ Verbose explanations ❌ Historical context dumping ❌ Overlapping tool definitions ❌ Premature information loading ❌ Vague instructions ("might", "could", "should")
For full standards: @plugins/meta-work/docs/HOW_TO_PROMPT_ENGINEERING.md
Anthropic's "Effective Context Engineering for AI Agents"
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