/Prompting

name: Prompting
version: 1.1.27
description: "Meta-prompting standard library for generating, optimizing, and composing prompts programmatically via Standards, Handlebars Templates, and Tools; output is always a prompt to use 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)."

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.

Ideal-State Prompting — the Default Standard

**Every prompt this library generates or optimizes articulates the ideal state, not the procedure.** Say WHAT done looks like (as testable outcomes), the CONSTRAINTS, and the high-quality TOOLS available — then trust the model to find HOW. Reasoning choreography ("first analyze, then consider, then decide") is BPE-violating scaffolding: it caps a capable model and rots as models improve. Ideal-state prompting is *more* precise, not vaguer — the specificity moves to the outcome.

Four keep-classes are legitimate HOW and survive the cut: **safety-gate**, **verified-gotcha**, **tool-contract**, **output-format-contract**. Deterministic tools (`*.ts`) are exempt. The test for any procedural line: *would a smarter model make this rule unnecessary?* Yes → cut; No → it's a keep-class. Full standard: `Standards.md` § Ideal-State Prompting.

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 (compose an agent brief)

// Render a structured agent brief from data before launching general-purpose
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: Render an Agent Brief from Data

// Render a structured agent brief, then launch general-purpose with it
const brief = renderTemplate('Primitives/Briefing.hbs', {
  agent: { name: 'Skeptical Security Reviewer', role: 'auth bypass and input v