prompt-engineer

Purpose

This skill transforms raw, unstructured user prompts into highly optimized prompts using established prompting frameworks. It analyzes user intent, identifies task complexity, and intelligently selects the most appropriate framework(s) to maximize Claude/ChatGPT output quality.

The skill operates in "magic mode" - it works silently behind the scenes, only interacting with users when clarification is critically needed. Users receive polished, ready-to-use prompts without technical explanations or framework jargon.

This is a universal skill that works in any terminal context, not limited to Obsidian vaults or specific project structures.

When to Use

Invoke this skill when:

• User provides a vague or generic prompt (e.g., "help me code Python")
• User has a complex idea but struggles to articulate it clearly
• User's prompt lacks structure, context, or specific requirements
• Task requires step-by-step reasoning (debugging, analysis, design)
• User needs a prompt for a specific AI task but doesn't know prompting frameworks
• User wants to improve an existing prompt's effectiveness
• User asks variations of "how do I ask AI to..." or "create a prompt for..."

Workflow

Step 1: Analyze Intent

Objective: Understand what the user truly wants to accomplish.

Actions:

1. Read the raw prompt provided by the user
2. Detect task characteristics:
   • Type: coding, writing, analysis, design, learning, planning, decision-making, creative, etc.
   • Complexity: simple (one-step), moderate (multi-step), complex (requires reasoning/design)
   • Clarity: clear intention vs. ambiguous/vague
   • Domain: technical, business, creative, academic, personal, etc.
3. Identify implicit requirements:
   • Does user need examples?
   • Is output format specified?
   • Are there constraints (time, resources, scope)?
   • Is this exploratory or execution-focused?

Detection Patterns:

• Simple tasks: Short prompts (<50 chars), single verb, no context
• Complex tasks: Long prompts (>200 chars), multiple requirements, conditional logic
• Ambiguous tasks: Generic verbs ("help", "improve"), missing object/context
• Structured tasks: Mentions steps, phases, deliverables, stakeholders

Step 2: Ask Clarifying Questions (Conditional)

Objective: Gather missing information only when it is critical to framework selection or prompt quality.

Trigger Conditions — ask only if:

• Task type is completely ambiguous (cannot determine coding vs. writing vs. analysis)
• Target audience is unknown and materially affects the output
• Scope is undefined and choosing wrong scope would invalidate the prompt
• Requested output format conflicts or is missing and cannot be inferred

Question Limits:

• Maximum 3 questions per invocation
• Combine related questions into one when possible
• If enough context exists, skip this step entirely (most cases)

Example Clarifying Exchange:

User: "help me with AI"

Step 2 (triggered — task type ambiguous):
"To craft the best prompt, I need one quick clarification:
1. What do you want to do with AI — build something, learn about it, or use an AI tool for a task?"

Critical Rule: When in doubt, skip clarification and generate the best prompt with available context. Over-asking breaks the "magic mode" experience.

Step 3: Select Framework(s)

Objective: Map task characteristics to optimal prompting framework(s).

Framework Mapping Logic:

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