# 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.

- Skill: `dallascrilley/prompting` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add dallascrilley/prompting`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dallascrilley/prompting/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: dallascrilley (https://skillmd.com/u/dallascrilley)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dallascrilley/prompting

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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
- [ ] 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 dumping
❌ Overlapping tool definitions
❌ Premature information loading
❌ Vague instructions ("might", "could", "should")

## Supplementary Resources
For full standards: `read $HOME/.claude/skills/prompting/CLAUDE.md`

## Based On
Anthropic's "Effective Context Engineering for AI Agents"