Prompting

Skill ComeOnOliver/skillshub/skills/aiskillstore/marketplace/aaronabuusama/prompting

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Install

npx -y skills add ComeOnOliver/skillshub --skill prompting

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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.

SKILL.md

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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 calculate_tax 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

Anti-Patterns

❌ Verbose explanations ❌ Historical context dumping ❌ Overlapping tool definitions ❌ Premature information loading ❌ Vague instructions ("might", "could", "should")

Supplementary Resources

For full standards: read ${PAI_DIR}/skills/prompting/CLAUDE.md

Based On

Anthropic's "Effective Context Engineering for AI Agents"