Prompting

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.

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.