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
- Installs
- 42
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/magentosh/superpowers --skill prompt-engineering- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads prompt-engineering automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Install the belt CLI skill:
npx skills add belt-sh/cli
Prompt Engineering Guide
Master prompt engineering for AI models via inference.sh CLI.
Quick Start
Requires inference.sh CLI (
belt). Install instructions
belt login
# Well-structured LLM prompt
belt 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."
}'
LLM Prompting
Basic Structure
[Role/Context] + [Task] + [Constraints] + [Output Format]
Role Prompting
belt 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
# 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
belt 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
belt 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
belt 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
belt 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
# Bad: vague
"a cat"
# Good: specific
belt app run falai/flux-dev --input '{
"prompt": "A fluffy orange tabby cat with green eyes, sitting on a vintage leather armchair"
}'
Style Keywords
belt 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
belt 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
belt 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
belt 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
belt 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
belt 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
belt 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
# Start broad
belt app run falai/flux-dev --input '{
"prompt": "A castle on a hill"
}'
# Add specifics
belt app run falai/flux-dev --input '{
"prompt": "A medieval stone castle on a grassy hill"
}'
# Add style
belt 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
belt 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
# First: analyze
belt 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
belt 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
belt 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
# 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@infsh-cli
Browse all apps: belt app list
FAQ
What does the prompt-engineering 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
How do I install prompt-engineering?
Run npx skills add https://github.com/magentosh/superpowers --skill prompt-engineering in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-engineering/ (personal) or .claude/skills/prompt-engineering/ (project).
Which AI agents can use prompt-engineering?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is prompt-engineering safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains prompt-engineering?
It is published by magentosh in magentosh/superpowers. It has 42 recorded installs.
More from magentosh/superpowers
ai-image-generation
magentosh/superpowers
Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image
ai-video-generation
magentosh/superpowers
Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, reference-to-video, video editing, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative, seedance, happyhorse
ai-avatar-video
magentosh/superpowers
Create AI avatar and talking head videos via inference.sh CLI. Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS). Also: OmniHuman, Fabric, PixVerse. Audio: Inworld TTS-2 (100+ languages, emotion steering for characters), ElevenLabs, Kokoro. Capabilities: audio-driven avatars, text-to-avatar, lipsync videos, talking head generation, virtual presenters, UGC content. Use for: AI presenters, explainer videos, virtual influencers, dubbing, marketing videos, UGC ads, gaming avatars, NPC dialogue. Triggers: ai avatar, talking head, lipsync, avatar video, virtual presenter, ai spokesperson, audio driven video, heygen alternative, synthesia alternative, talking avatar, lip sync, video avatar, ai presenter, digital human, ugc, ugc video, ugc ad, avatar ugc
twitter-automation
magentosh/superpowers
Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. Apps: x/post-tweet, x/post-create (with media), x/post-like, x/post-retweet, x/dm-send, x/user-follow. Capabilities: post tweets, schedule content, like posts, retweet, send DMs, follow users, get profiles. Use for: social media automation, content scheduling, engagement bots, audience growth, X API. Triggers: twitter api, x api, tweet automation, post to twitter, twitter bot, social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet
agent-browser
magentosh/superpowers
Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video
web-search
magentosh/superpowers
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available