---
name: prompting-14b3597b4b5d
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)

```typescript
// 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)

```yaml
# 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...
```

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

### Example 3: Custom Agent with Voice Mapping

```typescript
// 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

---

**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.


---
Source attribution: virtualian
Original project: https://github.com/virtualian/pai/blob/cd97056e594c9d5d83e96bb0fbd82185f77e7320/Releases/v2.5/.claude/skills/Prompting/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Daniel Miessler

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


# Mega AI Hub resource metadata

Creator / publisher: virtualian
Original project: https://github.com/virtualian/pai/blob/cd97056e594c9d5d83e96bb0fbd82185f77e7320/Releases/v2.5/.claude/skills/Prompting/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Data Scientist, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API
Tools: Curl, Handlebars
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 35d72d5d4ceb20aaa233916df702f0fd7b49e3da524033e54dfca26747c4eeb6