Claude Skill

prompt-engineering

Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. Use when creating prompts, optimizing existing prompts, or improving AI instructions.

LLM Mart · 0 points · 0 views 0 listing impressions 0 install-command copies
Virus-scanned Reviewed automatically before listing.

Full trust report

Download sabahattink-antigravity-fullstack-hq-skills_prompt-engineering-1acbfa7.zip · 1 KB
Part of sabahattink/antigravity-fullstack-hq — 27 skills

Install

skills CLI npx skills add https://github.com/sabahattink/antigravity-fullstack-hq/tree/main/skills/prompt-engineering
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install sabahattink-antigravity-fullstack-hq@llmmart
Git git clone https://github.com/sabahattink/antigravity-fullstack-hq.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole sabahattink/antigravity-fullstack-hq collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Prompt Engineering

Workflow

User Request
|
+-- "Create a prompt" --> EXPLORATION PHASE
+-- "Optimize this prompt" --> OPTIMIZATION PHASE
+-- "Fix this issue" --> ANALYSIS PHASE

Phase 1: Exploration

Before creating any prompt, understand:

  • What task will this prompt accomplish?
  • Who will use it?
  • What does success look like?
  • What are the constraints?

Phase 2: Analysis

Task Classification

Dimension Options
Complexity Simple vs multi-step
Output Creative vs analytical vs structured
Stakes High vs experimental

Strategy Selection

Task Type Approach
Simple Direct instructions
Complex Chain-of-thought
Creative Role setting
Structured Format specs + examples

Phase 3: Implementation

Version 1 - Minimal

  • Core instructions only
  • Test basic functionality

Version 2 - Enhanced

  • Add examples
  • Clarify ambiguities
  • Add constraints

Version 3+ - Optimized

  • Refine wording
  • Remove redundancy

Key Techniques

Role Setting

As an experienced code reviewer, analyze...

Chain-of-Thought

Think step-by-step:
1. First, identify...
2. Then, analyze...
3. Finally, conclude...

Few-Shot Learning

Example 1:
Input: "Great product"
Output: { "sentiment": "positive" }

Now analyze: "It was okay"

Explicit Constraints

- Limit to 3 paragraphs
- Focus on technical aspects only
- Do not include pricing

Prompt Template

## Context
[Background information]

## Role (Optional)
You are a [ROLE] with expertise in [DOMAIN].

## Task
[Clear instruction]

## Constraints
- Constraint 1
- Constraint 2

## Output Format
[Format specification]

## Examples (Optional)
[Input/Output examples]

Common Mistakes

Mistake Fix
Vague instructions Be specific
No examples Add 1-2 examples
Too many rules Simplify
No format spec Define output structure
Files (antigravity-fullstack-hq)
  • SKILL.md 2.2 KB
    ---
    name: prompt-engineering
    description: Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. Use when creating prompts, optimizing existing prompts, or improving AI instructions.
    ---
    
    # Prompt Engineering
    
    ## Workflow
    
    ```
    User Request
    |
    +-- "Create a prompt" --> EXPLORATION PHASE
    +-- "Optimize this prompt" --> OPTIMIZATION PHASE
    +-- "Fix this issue" --> ANALYSIS PHASE
    ```
    
    ## Phase 1: Exploration
    
    Before creating any prompt, understand:
    
    - What task will this prompt accomplish?
    - Who will use it?
    - What does success look like?
    - What are the constraints?
    
    ## Phase 2: Analysis
    
    ### Task Classification
    
    | Dimension | Options |
    |-----------|---------|
    | Complexity | Simple vs multi-step |
    | Output | Creative vs analytical vs structured |
    | Stakes | High vs experimental |
    
    ### Strategy Selection
    
    | Task Type | Approach |
    |-----------|----------|
    | Simple | Direct instructions |
    | Complex | Chain-of-thought |
    | Creative | Role setting |
    | Structured | Format specs + examples |
    
    ## Phase 3: Implementation
    
    ### Version 1 - Minimal
    - Core instructions only
    - Test basic functionality
    
    ### Version 2 - Enhanced
    - Add examples
    - Clarify ambiguities
    - Add constraints
    
    ### Version 3+ - Optimized
    - Refine wording
    - Remove redundancy
    
    ## Key Techniques
    
    ### Role Setting
    
    ```
    As an experienced code reviewer, analyze...
    ```
    
    ### Chain-of-Thought
    
    ```
    Think step-by-step:
    1. First, identify...
    2. Then, analyze...
    3. Finally, conclude...
    ```
    
    ### Few-Shot Learning
    
    ```
    Example 1:
    Input: "Great product"
    Output: { "sentiment": "positive" }
    
    Now analyze: "It was okay"
    ```
    
    ### Explicit Constraints
    
    ```
    - Limit to 3 paragraphs
    - Focus on technical aspects only
    - Do not include pricing
    ```
    
    ## Prompt Template
    
    ```markdown
    ## Context
    [Background information]
    
    ## Role (Optional)
    You are a [ROLE] with expertise in [DOMAIN].
    
    ## Task
    [Clear instruction]
    
    ## Constraints
    - Constraint 1
    - Constraint 2
    
    ## Output Format
    [Format specification]
    
    ## Examples (Optional)
    [Input/Output examples]
    ```
    
    ## Common Mistakes
    
    | Mistake | Fix |
    |---------|-----|
    | Vague instructions | Be specific |
    | No examples | Add 1-2 examples |
    | Too many rules | Simplify |
    | No format spec | Define output structure |
    

Comments (0)

Sign in to join the conversation.

No comments yet.

Reviews (0)

No reviews yet.

Related