Agent skill · writing content · aiskillstore
prompting
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.
Why this skill is useful
Provides specific standards and principles for prompt engineering that enhance clarity and effectiveness in AI interactions.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Prompting Skill When to Activate This Skill Prompt engineering questions Context engineering guidance AI agent design Prompt structure help Best practices for LLM prompts Agent configuration Core Philosophy Context engineering = Curating optimal set of tokens during LLM inference Primary Goal: Find smallest possible set of high-signal tokens that maximize desired outcomes Key Principles 1. Context is Finite Resource LLMs have limited "attention budget" Performance degrades as context grows Every token depletes capacity Treat context as precious 2. Optimize Signal-to-Noise Clear, direct language over verbose explanations Remove redundant information Focus on high-value tokens 3. Progressive Discovery Use lightweight identifiers vs full data dumps Load detailed info dynamically when needed Just-in-time information loading Markdown Structure Standards Use clear semantic sections: Background Information: Minimal essential context Instructions: Imperative voice, specific, actionable Examples: Show don't tell, concise, representative Constraints: Boundaries, limitations, success criteria Writing Style Clarity Over Completeness ✅ Good: "Validate input before processing" ❌ Bad: "You should always make sure to validate..." Be Direct ✅ Good: "Use calculatetax tool with amount and jurisdiction" ❌ Bad: "You might want to consider using..." Use Structured Lists ✅ Good: Bulleted constraints ❌ Bad: Paragraph of requirements Context Management Just-in-Time Loading Don't load full data dumps - use references and load when needed Structured Note-Taking Persist important info outside context window Sub-Agent Architecture Delegate subtasks to specialized agents with minimal context Best Practices Checklist [ ] Uses Markdown headers for organization [ ] Clear, direct, minimal language [ ] No redundant information [ ] Actionable instructions [ ] Concrete examples [ ] Clear constraints [ ] Just-in-time loading when appropriate Anti-Patterns ❌ Verbose explanations ❌ Historical context dump …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/prompting