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prompt-engineering及时工程

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

总安装

26,388

周安装

657

GitHub Stars

2

下载量

9,044
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:prompt-engineering(及时工程)
来源仓库:https://github.com/skill-zero/s
仓库路径:skills/prompt-engineering
安装命令:
npx skills add skill-zero/s --skill "prompt-engineering"
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add skill-zero/s --skill "prompt-engineering"

简介

掌握人工智能模型的即时工程:法学硕士、图像生成器、视频模型。技巧:思路链、少击、系统提示、负面提示。模型:Claude、GPT-4、Gemini、FLUX、Veo、稳定扩散提示。用于:更好的人工智能输出、一致的结果、复杂的任务、优化。触发点:提示工程、如何提示、更好的提示、提示技巧、提示指南、llm提示、图片提示、ai提示、提示优化、提示模板、提示结构、有效提示、提示技巧

SKILL.md

name
prompt-engineering
description
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 CLI.

Prompt Engineering Guide

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:\
\

user_input = request.args.get(\"query\")\ result = db.execute(f\"SELECT * FROM users WHERE name = {user_input}\")\

\
Provide specific issues and fixes."
}'

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:\
\
A 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?\
\
Think 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:\
\
Example 1:\
Input: gonna send u the report tmrw\
Output: I will send you the report tomorrow.\
\
Example 2:\
Input: cant make the meeting, something came up\
Output: I apologize, but I will be unable to attend the meeting due to an unforeseen circumstance.\
\
Now convert:\
Input: 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).\
\
Reviews:\
1. \"Great product, fast shipping!\"\
2. \"Meh, its okay I guess\"\
3. \"Worst purchase ever, total waste of money\"\
\
Return 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.\
\
[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:\
\
\"John Smith, CEO of TechCorp, announced yesterday that the company raised $50 million in Series B funding. The round was led by Venture Partners.\"\
\
Schema:\
{\
  \"person\": string,\
  \"title\": string,\
  \"company\": string,\
  \"event\": string,\
  \"amount\": string,\
  \"investor\": string\
}"
}'

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

MistakeProblemFix
Too vagueUnpredictable outputAdd specifics
Too longModel loses focusPrioritize key info
ConflictingConfuses modelRemove contradictions
No formatInconsistent outputSpecify format
No examplesUnclear expectationsAdd 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

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

66.19%
按下载量换算5,986

Cursor

25.31%
按下载量换算2,289

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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