Prompting - Meta-Prompting & Template System Skill

Customization

Before executing, check for user customizations at: ~/.opencode/skills/CORE/USER/SKILLCUSTOMIZATIONS/Prompting/

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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)

Voice Notification

When executing a workflow, do BOTH:

Send voice notification:

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

Output text notification:

Running the **WorkflowName** workflow from the **Prompting** skill...

Claude 4.x behavioral characteristics
Multi-context window workflows
Agentic coding best practices
Output format control
The Ultimate Prompt Template

2. Templates/ - Five Core Primitives

The templating system enables prompts that write prompts - dynamic composition where structure is fixed but content is parameterized.

Directory Structure:

Templates/
├── Primitives/       # Five core template patterns
│   ├── Roster.hbs    # Agent/skill definitions from data
│   ├── Voice.hbs     # Personality calibration settings
│   ├── Structure.hbs # Multi-step workflow patterns
│   ├── Briefing.hbs  # Agent context handoff
│   └── Gate.hbs      # Validation checklists
├── Examples/         # Sample data and usage
└── (Evals/)          # Eval-specific templates (from Evals skill)

The Five Primitives:

| Primitive | Purpose | Use Case | |-----------|---------|----------| | ROSTER | Data-driven definitions | 32 RedTeam agents, 83 skills, voice configs | | VOICE | Personality calibration | Voice parameters, rate, archetype mapping | | STRUCTURE | Workflow patterns | Phased analysis, round-based debate, pipelines | | BRIEFING | Agent context handoff | Research queries, delegation, task assignment | | GATE | Validation checklists | Quality gates, completion checks, verification |

3. Tools/

RenderTemplate.ts - Core rendering engine

bun run ~/.opencode/skills/Prompting/Tools/RenderTemplate.ts \
  --template Primitives/Briefing.hbs \
  --data path/to/data.yaml \
  --output path/to/output.md

ValidateTemplate.ts - Template syntax checker

bun run ~/.opencode/skills/Prompting/Tools/ValidateTemplate.ts \
  --template Primitives/Briefing.hbs \
  --data path/to/sample-data.yaml

4. Template Syntax

The system uses Handlebars notation (Anthropic's official syntax):

| Syntax | Purpose | Example | |--------|---------|---------| | {{variable}} | Simple interpolation | Hello {{name}} | | {{object.property}} | Nested access | {{agent.voice_id}} | | {{#each items}}...{{/each}} | Iteration | List generation | | {{#if condition}}...{{/if}} | Conditional | Optional sections | | {{> partial}} | Include partial | Reusable components |

Usage Examples

Example 1: Using Briefing Template (Agent Skill)

// skills/Agents/Tools/AgentFactory.ts
import { renderTemplate } from '~/.opencode/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:

| 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.

Agent Skills: Prompting - Meta-Prompting & Template System

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