name: Prompting
description: Meta-prompting system for dynamic prompt generation using templates, standards, and patterns. USE WHEN meta-prompting, template generation, prompt optimization, or programmatic prompt composition.

Customization

Before executing, check for user customizations at:
~/.claude/skills/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:
   bash 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/AgentFactory.ts
import { renderTemplate } from '~/.claude/skills/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:

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

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.