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.
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sabahattink-antigravity-fullstack-hq-skills_prompt-engineering-1acbfa7.zip · 1 KB
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 |
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