pai-prompting-skill

name	PAI Prompting Skill
pack-id	danielmiessler-pai-prompting-skill-v2.3.0
version	2.3.0
author	danielmiessler
description	Meta-prompting system for dynamic prompt generation using Handlebars templates, Claude 4.x best practices, and the Fabric pattern system. Includes the Ultimate Prompt Template, five core primitives (Roster, Voice, Structure, Briefing, Gate), eval templates for LLM-as-Judge, and CLI tools for rendering and validation.
type	feature
purpose-type	productivity development automation
platform	claude-code
dependencies	pai-core-install (required) - Skills directory structure and routing pai-hook-system (optional) - For session startup context loading
keywords	prompting templates handlebars meta-prompting prompt-engineering fabric claude-4 context-engineering templating evals

PAI Prompting Skill (pai-prompting-skill)

Prompts that write prompts. Meta-prompting enables dynamic composition where structure is fixed but content is parameterized.

Installation: This pack is designed for AI-assisted installation. Give this directory to your AI and ask it to install using the wizard in INSTALL.md. The installation dynamically adapts to your system state. See AI-First Installation Philosophy for details.

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What This Pack Does

This Pack provides a complete prompt engineering system:

Prompt Engineering Standards

• Claude 4.x Best Practices (behavioral characteristics, tool patterns)
• Context engineering principles (token efficiency, signal optimization)
• The Ultimate Prompt Template (modular, validated structure)
• Fabric pattern integration (248 reusable prompts)

Template System

• Five core primitives: Roster, Voice, Structure, Briefing, Gate
• Five eval templates: Judge, Rubric, Comparison, Report, TestCase
• Handlebars-based templating (Anthropic's official syntax)
• Data-driven prompt generation from YAML
• Pre-configured data files (Agents, ValidationGates, VoicePresets)
• CLI tools for rendering and validation

Core Philosophy: Find the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.

Architecture

pai-prompting-skill/
├── README.md                    # This file
├── INSTALL.md                   # Installation instructions
├── VERIFY.md                    # Verification checklist
└── src/
    └── skills/
        └── Prompting/
            ├── SKILL.md         # Skill routing and overview
            ├── Standards.md     # Claude 4.x best practices
            ├── Templates/
            │   ├── README.md    # Template system docs
            │   ├── Primitives/  # Core templates (5 files)
            │   │   ├── Roster.hbs
            │   │   ├── Voice.hbs
            │   │   ├── Structure.hbs
            │   │   ├── Briefing.hbs
            │   │   └── Gate.hbs
            │   ├── Evals/       # LLM-as-Judge templates (5 files)
            │   │   ├── Judge.hbs
            │   │   ├── Rubric.hbs
            │   │   ├── Comparison.hbs
            │   │   ├── Report.hbs
            │   │   └── TestCase.hbs
            │   ├── Data/        # Pre-configured YAML data
            │   │   ├── Agents.yaml
            │   │   ├── ValidationGates.yaml
            │   │   └── VoicePresets.yaml
            │   └── Tools/       # Template CLI tools
            │       ├── RenderTemplate.ts
            │       ├── ValidateTemplate.ts
            │       └── package.json
            └── Tools/
                ├── RenderTemplate.ts
                └── ValidateTemplate.ts

Token Efficiency

The templating system reduces duplication significantly:

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

The Problem This Solves

Without Prompt Standards

1. Inconsistent Quality - Prompts vary wildly in effectiveness
2. Reinventing Wheels - Every prompt starts from scratch
3. Token Waste - Verbose prompts that don't improve results
4. Model Confusion - Using patterns that hurt Claude 4.x performance
5. No Validation - No way to verify prompt structure

Without Templates

1. Manual Repetition - Copy-pasting similar structures
2. Drift - Agent definitions diverge over time
3. Maintenance Burden - Updating 30 agents means 30 edits
4. No Separation - Structure and content entangled
5. Testing Difficulty - Can't A/B test structure vs. content

The Core Insight

Prompt engineering research shows:

• 10-90% performance variation based on structure choices
• Few-shot examples add +25-90% improvement (1-3 optimal)
• Clear instructions reduce ambiguity and improve task completion
• Structured organization provides consistent performance gains

This isn't opinion—it's validated by 1,500+ academic papers and production systems.

Five Core Primitives

Primitive	Purpose	Use Case
ROSTER	Data-driven definitions	Agent personalities, skill listings
VOICE	Personality calibration	Voice parameters, trait settings
STRUCTURE	Workflow patterns	Phased analysis, debate rounds
BRIEFING	Agent context handoff	Task delegation, research queries
GATE	Validation checklists	Quality checks, completion criteria

Quick Start

# Render a template
bun run $PAI_DIR/skills/Prompting/Tools/RenderTemplate.ts \
  --template Primitives/Briefing.hbs \
  --data path/to/data.yaml \
  --output path/to/output.md

# Validate template syntax
bun run $PAI_DIR/skills/Prompting/Tools/ValidateTemplate.ts \
  --template Primitives/Briefing.hbs

Credits

• Author: Daniel Miessler
• Origin: Extracted from production PAI system (2024-2026)
• License: MIT

Acknowledgments

• Anthropic - Claude 4.x Best Practices, context engineering research
• IndyDevDan - Meta-prompting concepts and inspiration
• Daniel Miessler - Fabric pattern system (248 reusable prompts)
• Academic Community - "The Prompt Report", "The Prompt Canvas"