prompting

name	Prompting
description	Meta-prompting system that generates optimized prompts using templates, standards, and patterns. Produces structured prompts with role, context, and output format. USE WHEN meta-prompting, template generation, prompt optimization, programmatic prompt composition, render template, validate template, prompt engineering.

Customization

Before executing, check for user customizations at: ~/.claude/PAI/USER/SKILLCUSTOMIZATIONS/Prompting/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

1. Send voice notification:

   curl -s -X POST http://localhost:8888/notify \
     -H "Content-Type: application/json" \
     -d '{"message": "Running the WORKFLOWNAME workflow in the Prompting skill to ACTION"}' \
     > /dev/null 2>&1 &
   

2. Output text notification:

   Running the **WorkflowName** workflow in the **Prompting** skill to ACTION...
   

This is not optional. Execute this curl command immediately upon skill invocation.

Prompting - Meta-Prompting & Template System

Invoke when: meta-prompting, template generation, prompt optimization, programmatic prompt composition, creating dynamic agents, generating structured prompts from data.

Overview

The Prompting skill owns ALL prompt engineering concerns:

• Standards - Anthropic best practices, Claude 4.x patterns, empirical research
• Templates - Handlebars-based system for programmatic prompt generation
• Tools - Template rendering, validation, and composition utilities
• Patterns - Reusable prompt primitives and structures

This is the "standard library" for prompt engineering - other skills reference these resources when they need to generate or optimize prompts.

Core Components

1. Standards.md

Complete prompt engineering documentation based on:

• Anthropic's Claude 4.x Best Practices (November 2025)
• Context engineering principles
• The Fabric prompt pattern system
• 1,500+ academic papers on prompt optimization

Key Topics:

• Markdown-first design (NO XML tags)

Usage Examples

Example 1: Using Briefing Template (Agent Skill)

// skills/Agents/Tools/ComposeAgent.ts
import { renderTemplate } from '~/.claude/skills/Utilities/Prompting/Tools/RenderTemplate.ts';

const prompt = renderTemplate('Primitives/Briefing.hbs', {
  briefing: { type: 'research' },
  agent: { id: 'EN-1', name: 'Skeptical Thinker', personality: {...} },
  task: { description: 'Analyze security architecture', questions: [...] },
  output_format: { type: 'markdown' }
});

Example 2: Using Structure Template (Workflow)

# Data: phased-analysis.yaml
phases:
  - name: Discovery
    purpose: Identify attack surface
    steps:
      - action: Map entry points
        instructions: List all external interfaces...
  - name: Analysis
    purpose: Assess vulnerabilities
    steps:
      - action: Test boundaries
        instructions: Probe each entry point...

bun run RenderTemplate.ts \
  --template Primitives/Structure.hbs \
  --data phased-analysis.yaml

Example 3: Custom Agent with Voice Mapping

// Generate specialized agent with appropriate voice
const agent = composeAgent(['security', 'skeptical', 'thorough'], task, traits);
// Returns: { name, traits, voice: 'default', voiceId: 'VOICE_ID...' }

Integration with Other Skills

Agents Skill

• Uses Templates/Primitives/Briefing.hbs for agent context handoff
• Uses RenderTemplate.ts to compose dynamic agents
• Maintains agent-specific template: Agents/Templates/DynamicAgent.hbs

Evals Skill

• Uses eval-specific templates: Judge, Rubric, TestCase, Comparison, Report
• Leverages RenderTemplate.ts for eval prompt generation
• Eval templates may be stored in Evals/Templates/ but use Prompting's engine

Development Skill

• References Standards.md for prompt best practices
• Uses Structure.hbs for workflow patterns
• Applies Gate.hbs for validation checklists

Token Efficiency

The templating system eliminated ~35,000 tokens (65% reduction) across PAI:

Area	Before	After	Savings
SKILL.md Frontmatter	20,750	8,300	60%
Agent Briefings	6,400	1,900	70%
Voice Notifications	6,225	725	88%
Workflow Steps	7,500	3,000	60%
TOTAL	~53,000	~18,000	65%

Best Practices

1. Separation of Concerns

• Templates: Structure and formatting only
• Data: Content and parameters (YAML/JSON)
• Logic: Rendering and validation (TypeScript)

2. Keep Templates Simple

• Avoid complex logic in templates
• Use Handlebars helpers for transformations
• Business logic belongs in TypeScript, not templates

3. DRY Principle

• Extract repeated patterns into partials
• Use presets for common configurations
• Single source of truth for definitions

4. Version Control

• Templates and data in separate files
• Track changes independently
• Enable A/B testing of structures

References

Primary Documentation:

• Standards.md - Complete prompt engineering guide
• Templates/README.md - Template system overview (if preserved)
• Tools/RenderTemplate.ts - Implementation details

Research Foundation:

• Anthropic: "Claude 4.x Best Practices" (November 2025)
• Anthropic: "Effective Context Engineering for AI Agents"
• Anthropic: "Prompt Templates and Variables"
• The Fabric System (January 2024)
• "The Prompt Report" - arXiv:2406.06608
• "The Prompt Canvas" - arXiv:2412.05127

Related Skills:

• Agents - Dynamic agent composition
• Evals - LLM-as-Judge prompting
• Development - Spec-driven development patterns

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Philosophy: Prompts that write prompts. Structure is code, content is data. Meta-prompting enables dynamic composition where the same template with different data generates specialized agents, workflows, and evaluation frameworks. This is core PAI DNA - programmatic prompt generation at scale.