prompt-engineering
Master prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Models: Claude, GPT-4, Gemini, FLUX, Veo, Stable Diffusion prompting. Use for: better AI outputs, consistent results, complex tasks, optimization. Triggers: prompt engineering, how to prompt, better prompts, prompt tips, prompting guide, llm prompting, image prompt, ai prompting, prompt optimization, prompt template, prompt structure, effective prompts, prompt techniques
Best use case
prompt-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Master prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Models: Claude, GPT-4, Gemini, FLUX, Veo, Stable Diffusion prompting. Use for: better AI outputs, consistent results, complex tasks, optimization. Triggers: prompt engineering, how to prompt, better prompts, prompt tips, prompting guide, llm prompting, image prompt, ai prompting, prompt optimization, prompt template, prompt structure, effective prompts, prompt techniques
Teams using prompt-engineering should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-engineering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-engineering Compares
| Feature / Agent | prompt-engineering | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Master prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Models: Claude, GPT-4, Gemini, FLUX, Veo, Stable Diffusion prompting. Use for: better AI outputs, consistent results, complex tasks, optimization. Triggers: prompt engineering, how to prompt, better prompts, prompt tips, prompting guide, llm prompting, image prompt, ai prompting, prompt optimization, prompt template, prompt structure, effective prompts, prompt techniques
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Prompt Engineering Guide
Master prompt engineering for AI models via [inference.sh](https://inference.sh) CLI.

## Quick Start
```bash
curl -fsSL https://cli.inference.sh | sh && infsh login
# Well-structured LLM prompt
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "You are a senior software engineer. Review this code for security vulnerabilities:\n\n```python\nuser_input = request.args.get(\"query\")\nresult = db.execute(f\"SELECT * FROM users WHERE name = {user_input}\")\n```\n\nProvide specific issues and fixes."
}'
```
> **Install note:** The [install script](https://cli.inference.sh) only detects your OS/architecture, downloads the matching binary from `dist.inference.sh`, and verifies its SHA-256 checksum. No elevated permissions or background processes. [Manual install & verification](https://dist.inference.sh/cli/checksums.txt) available.
## LLM Prompting
### Basic Structure
```
[Role/Context] + [Task] + [Constraints] + [Output Format]
```
### Role Prompting
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "You are an expert data scientist with 15 years of experience in machine learning. Explain gradient descent to a beginner, using simple analogies."
}'
```
### Task Clarity
```bash
# Bad: vague
"Help me with my code"
# Good: specific
"Debug this Python function that should return the sum of even numbers from a list, but returns 0 for all inputs:
def sum_evens(numbers):
total = 0
for n in numbers:
if n % 2 == 0:
total += n
return total
Identify the bug and provide the corrected code."
```
### Chain-of-Thought
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Solve this step by step:\n\nA store sells apples for $2 each and oranges for $3 each. If someone buys 5 fruits and spends $12, how many of each fruit did they buy?\n\nThink through this step by step before giving the final answer."
}'
```
### Few-Shot Examples
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Convert these sentences to formal business English:\n\nExample 1:\nInput: gonna send u the report tmrw\nOutput: I will send you the report tomorrow.\n\nExample 2:\nInput: cant make the meeting, something came up\nOutput: I apologize, but I will be unable to attend the meeting due to an unforeseen circumstance.\n\nNow convert:\nInput: hey can we push the deadline back a bit?"
}'
```
### Output Format Specification
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Analyze the sentiment of these customer reviews. Return a JSON array with objects containing \"text\", \"sentiment\" (positive/negative/neutral), and \"confidence\" (0-1).\n\nReviews:\n1. \"Great product, fast shipping!\"\n2. \"Meh, its okay I guess\"\n3. \"Worst purchase ever, total waste of money\"\n\nReturn only valid JSON, no explanation."
}'
```
### Constraint Setting
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Summarize this article in exactly 3 bullet points. Each bullet must be under 20 words. Focus only on actionable insights, not background information.\n\n[article text]"
}'
```
## Image Generation Prompting
### Basic Structure
```
[Subject] + [Style] + [Composition] + [Lighting] + [Technical]
```
### Subject Description
```bash
# Bad: vague
"a cat"
# Good: specific
infsh app run falai/flux-dev --input '{
"prompt": "A fluffy orange tabby cat with green eyes, sitting on a vintage leather armchair"
}'
```
### Style Keywords
```bash
infsh app run falai/flux-dev --input '{
"prompt": "Portrait photograph of a woman, shot on Kodak Portra 400 film, soft natural lighting, shallow depth of field, nostalgic mood, analog photography aesthetic"
}'
```
### Composition Control
```bash
infsh app run falai/flux-dev --input '{
"prompt": "Wide establishing shot of a cyberpunk city skyline at night, rule of thirds composition, neon signs in foreground, towering skyscrapers in background, rain-slicked streets"
}'
```
### Quality Keywords
```
photorealistic, 8K, ultra detailed, sharp focus, professional,
masterpiece, high quality, best quality, intricate details
```
### Negative Prompts
```bash
infsh app run falai/flux-dev --input '{
"prompt": "Professional headshot portrait, clean background",
"negative_prompt": "blurry, distorted, extra limbs, watermark, text, low quality, cartoon, anime"
}'
```
## Video Prompting
### Basic Structure
```
[Shot Type] + [Subject] + [Action] + [Setting] + [Style]
```
### Camera Movement
```bash
infsh app run google/veo-3-1-fast --input '{
"prompt": "Slow tracking shot following a woman walking through a sunlit forest, golden hour lighting, shallow depth of field, cinematic, 4K"
}'
```
### Action Description
```bash
infsh app run google/veo-3-1-fast --input '{
"prompt": "Close-up of hands kneading bread dough on a wooden surface, flour dust floating in morning light, slow motion, cozy baking aesthetic"
}'
```
### Temporal Keywords
```
slow motion, timelapse, real-time, smooth motion,
continuous shot, quick cuts, frozen moment
```
## Advanced Techniques
### System Prompts
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"system": "You are a helpful coding assistant. Always provide code with comments. If you are unsure about something, say so rather than guessing.",
"prompt": "Write a Python function to validate email addresses using regex."
}'
```
### Structured Output
```bash
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Extract information from this text and return as JSON:\n\n\"John Smith, CEO of TechCorp, announced yesterday that the company raised $50 million in Series B funding. The round was led by Venture Partners.\"\n\nSchema:\n{\n \"person\": string,\n \"title\": string,\n \"company\": string,\n \"event\": string,\n \"amount\": string,\n \"investor\": string\n}"
}'
```
### Iterative Refinement
```bash
# Start broad
infsh app run falai/flux-dev --input '{
"prompt": "A castle on a hill"
}'
# Add specifics
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill"
}'
# Add style
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill, dramatic sunset sky, fantasy art style, epic composition"
}'
# Add technical
infsh app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill, dramatic sunset sky, fantasy art style by Greg Rutkowski, epic composition, 8K, highly detailed"
}'
```
### Multi-Turn Reasoning
```bash
# First: analyze
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Analyze this business problem: Our e-commerce site has a 70% cart abandonment rate. List potential causes."
}'
# Second: prioritize
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Given these causes of cart abandonment: [previous output], rank them by likely impact and ease of fixing. Format as a priority matrix."
}'
# Third: action plan
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "For the top 3 causes identified, provide specific A/B tests we can run to validate and fix each issue."
}'
```
## Model-Specific Tips
### Claude
- Excels at nuanced instructions
- Responds well to role-playing
- Good at following complex constraints
- Prefers explicit output formats
### GPT-4
- Strong at code generation
- Works well with examples
- Good structured output
- Responds to "let's think step by step"
### FLUX
- Detailed subject descriptions
- Style references work well
- Lighting keywords important
- Negative prompts supported
### Veo
- Camera movement keywords
- Cinematic language works well
- Action descriptions important
- Include temporal context
## Common Mistakes
| Mistake | Problem | Fix |
|---------|---------|-----|
| Too vague | Unpredictable output | Add specifics |
| Too long | Model loses focus | Prioritize key info |
| Conflicting | Confuses model | Remove contradictions |
| No format | Inconsistent output | Specify format |
| No examples | Unclear expectations | Add few-shot |
## Prompt Templates
### Code Review
```
Review this [language] code for:
1. Bugs and logic errors
2. Security vulnerabilities
3. Performance issues
4. Code style/best practices
Code:
[code]
For each issue found, provide:
- Line number
- Issue description
- Severity (high/medium/low)
- Suggested fix
```
### Content Writing
```
Write a [content type] about [topic].
Audience: [target audience]
Tone: [formal/casual/professional]
Length: [word count]
Key points to cover:
1. [point 1]
2. [point 2]
3. [point 3]
Include: [specific elements]
Avoid: [things to exclude]
```
### Image Generation
```
[Subject with details], [setting/background], [lighting type],
[art style or photography style], [composition], [quality keywords]
```
## Related Skills
```bash
# Video prompting guide
npx skills add inference-sh/skills@video-prompting-guide
# LLM models
npx skills add inference-sh/skills@llm-models
# Image generation
npx skills add inference-sh/skills@ai-image-generation
# Full platform skill
npx skills add inference-sh/skills@inference-sh
```
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