PaperclipOrg
Claude Agent Skill · by Inferen Sh

Prompt Engineering

A comprehensive guide for getting better outputs from AI models through structured prompting techniques. Covers role-based prompting, chain-of-thought reasoning

Install
Terminal · npx
$npx skills add https://github.com/inferen-sh/skills --skill prompt-engineering
Works with Paperclip

How Prompt Engineering fits into a Paperclip company.

Prompt Engineering drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

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Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

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Source file
SKILL.md340 lines
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---name: prompt-engineeringdescription: "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"allowed-tools: Bash(infsh *)--- # Prompt Engineering Guide Master prompt engineering for AI models via [inference.sh](https://inference.sh) CLI. ![Prompt Engineering Guide](https://cloud.inference.sh/app/files/u/4mg21r6ta37mpaz6ktzwtt8krr/01kgvftjwhby36trvaj66bwzcf.jpeg) ## Quick Start > Requires inference.sh CLI (`infsh`). [Install instructions](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md) ```bashinfsh login # Well-structured LLM promptinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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: specificinfsh app run falai/flux-dev --input '{  "prompt": "A fluffy orange tabby cat with green eyes, sitting on a vintage leather armchair"}'``` ### Style Keywords ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 ```bashinfsh 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 broadinfsh app run falai/flux-dev --input '{  "prompt": "A castle on a hill"}' # Add specificsinfsh app run falai/flux-dev --input '{  "prompt": "A medieval stone castle on a grassy hill"}' # Add styleinfsh app run falai/flux-dev --input '{  "prompt": "A medieval stone castle on a grassy hill, dramatic sunset sky, fantasy art style, epic composition"}' # Add technicalinfsh 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: analyzeinfsh 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: prioritizeinfsh 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 planinfsh 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 errors2. Security vulnerabilities3. Performance issues4. 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 guidenpx skills add inference-sh/skills@video-prompting-guide # LLM modelsnpx skills add inference-sh/skills@llm-models # Image generationnpx skills add inference-sh/skills@ai-image-generation # Full platform skillnpx skills add inference-sh/skills@infsh-cli``` Browse all apps: `infsh app list`