prompting

## Customization **Before executing, check for user customizations at:** `~/.claude/skills/CORE/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. # 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:** 1. **Send voice notification**: ```bash 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 & ``` 2. **Output text notification**: ``` Running the **WorkflowName** workflow from the **Prompting** skill... ``` **Full documentation:** `~/.claude/skills/CORE/SkillNotifications.md` - 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 ```bash bun run ~/.claude/skills/Prompting/Tools/RenderTemplate.ts \ --template Primitives/Briefing.hbs \ --data path/to/data.yaml \ --output path/to/output.md ``` **ValidateTemplate.ts** - Template syntax checker ```bash bun run ~/.claude/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) ```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.