Prompt Engineering

Master prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Mode...

Security Scan

The skill is largely coherent as a prompt-engineering guide, but its runtime instructions tell users to download and run a third‑party CLI and to authenticate to external inference endpoints while the skill metadata declares no credentials or install requirements — this mismatch and the use of a remote install script are concerning.

Purpose & Capability

The name and description match the SKILL.md: the file is a prompt-engineering guide with examples for LLM, image, and video prompting and demonstrates usage of an external CLI (infsh). There are no unrelated declared requirements (no unexpected cloud creds, etc.).

Instruction Scope

The instructions repeatedly direct the user/agent to install and use the third‑party CLI (infsh) and to call external inference endpoints (openrouter, falai, google/veo, and inference.sh). That means user prompts and any data sent to those commands will be transmitted to third parties — a data‑exfiltration risk if sensitive data is used. The guide also instructs a curl | sh install flow (download-and-execute), which expands the agent's attack surface and should be treated cautiously.

Install Mechanism

Although the skill bundle itself has no install spec, the SKILL.md instructs running a remote installer via curl -fsSL https://cli.inference.sh | sh and downloading binaries from dist.inference.sh. Running a remote script piped to sh is a high-risk pattern; the file claims checksums are available (some mitigation), but the installer source is an external domain not documented in the skill metadata and the skill does not embed the checksum verification steps or show verifying before execution.

Credentials

The registry metadata lists no required environment variables or credentials, yet the guide calls out 'infsh login' and demonstrates invoking third‑party models that will require authentication/keys. This mismatch reduces transparency: users/agents must supply credentials (or interactively login) to use the CLI, but the skill does not declare what secrets will be needed or how they are used/stored.

Persistence & Privilege

The skill itself is instruction-only and not marked 'always'. However, following the guide installs a third‑party CLI binary that persists on disk and can be invoked later. The skill does not request system-wide config changes or modify other skills, but installation is an explicit action with potential long-lived effects.

What to consider before installing

This guide is coherent for learning prompt engineering, but exercise caution before following its runtime instructions. Do not run curl | sh blindly — inspect the install script at https://cli.inference.sh first and verify checksums from the claimed checksum URL. Understand that using the shown commands will send whatever you include in prompts to third‑party inference services (inference.sh/openrouter/falai/google), so never include secrets, passwords, private keys, or sensitive data in prompts. If you prefer less risk, consider using a vetted package manager, a self‑hosted/local model, or reading the guide without installing the CLI. If you need to use the CLI, confirm the provider's privacy/security policy and where credentials are stored (local config, environment variables, or remote storage) before logging in.

Prompt Engineering Guide

Master prompt engineering for AI models via inference.sh CLI.

Quick Start

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 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 available.

LLM Prompting

Basic Structure

[Role/Context] + [Task] + [Constraints] + [Output Format]

Role Prompting

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

# 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

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

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

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

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

# 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

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

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

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

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

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

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

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

# 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

# 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

# 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

Browse all apps: infsh app list