prompting

name	Prompting
description	Meta-prompting standard library — the LifeOS system for generating, optimizing, and composing prompts programmatically. Three pillars: Standards (Anthropic best practices, context engineering, Fabric patterns); Templates (Handlebars — Briefing, Structure, Gate, Roster, Voice, plus eval templates Judge, Rubric, TestCase, Comparison, Report used by Agents/Evals; the Agents skill keeps its own DynamicAgent.hbs); Tools (RenderTemplate.ts, data-content separation). Philosophy: prompts that write prompts — structure is code, content is data. Output is always a prompt to be used elsewhere, not final content. USE WHEN meta-prompting, template generation, prompt optimization, prompt engineering, write a prompt, create system prompt, Handlebars template, eval prompt, judge prompt. NOT FOR generating final content (use the appropriate domain skill).
effort	medium

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/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:

   curl -s -X POST http://localhost:31337/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

What It Does

Generates, optimizes, and composes prompts programmatically. It's the standard library for prompt engineering — other skills call it when they need to build or improve a prompt. The output is always a prompt to be used elsewhere, never the final content itself.

Invoke when: meta-prompting, template generation, prompt optimization, programmatic prompt composition, creating dynamic agents, generating structured prompts from data.

The Problem

Prompt engineering tends to get copy-pasted and rewritten by hand across every skill that needs it, so the same patterns drift apart and best practices live in one person's head. When you want to compose a prompt from data — spin up a custom agent, build an eval judge, generate a phased workflow — there's no clean way to separate the structure from the content. This skill makes structure code and content data: one Handlebars template plus different data renders specialized agents, workflows, and eval frameworks, and the engineering standards live in one place every skill can reference.

How It Works

Three pillars carry the work:

• Standards - Anthropic best practices, Claude 4.x patterns, empirical research (markdown-first design, context engineering, the Fabric pattern system, 1,500+ academic papers on prompt optimization). Full guide in Standards.md.
• Templates - Handlebars-based system for programmatic prompt generation: Primitives (Briefing, Structure, Gate, Roster, Voice) plus eval templates (Judge, Rubric, TestCase, Comparison, Report). The agent-specific DynamicAgent.hbs lives in the Agents skill (Agents/Templates/DynamicAgent.hbs), not here.
• Tools - Template rendering (RenderTemplate.ts), validation, and data-content separation.

Workflow Routing

Library skill — no Workflows/ directory. Requests route to the rendering tools and reference docs:

Trigger	Workflow	File
Render a template / compose a prompt from data / Handlebars template	RenderTemplate (tool)	Tools/RenderTemplate.ts
Validate a template	ValidateTemplate (tool)	Tools/ValidateTemplate.ts
Prompt engineering standards / best practices / prompt optimization	Standards (reference)	Standards.md

Examples

Example 1: Using Briefing Template (Agent Skill)

// skills/Agents/Tools/ComposeAgent.ts
import { renderTemplate } from '${LIFEOS_SKILL_DIR}/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 LifeOS:

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. DRY Principle

• Extract repeated patterns into partials
• Use presets for common configurations
• Single source of truth for definitions

3. 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
• 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

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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 LifeOS DNA - programmatic prompt generation at scale.

Gotchas

• Meta-prompting generates PROMPTS, not content. The output is a prompt that gets used elsewhere — not the final deliverable.
• Templates should be model-agnostic. Don't write prompts that depend on specific model quirks.
• Test generated prompts before declaring them ready. A prompt that looks good may perform poorly.

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Prompting","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.