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Ring:Engineering Prompts

Organization
LerianStudio
ring:engineering-prompts

Expert prompt engineering and optimization for LLMs and AI systems. Covers core patterns (zero-shot, few-shot, CoT, role-playing, constitutional, tree-of-thoughts), common use cases, and a three-phase process. Use when crafting or optimizing prompts for AI systems. Skip when the prompt is trivial or already performing well.

Overview

PublisherLerianStudio
Repositoryring
Skill namering:engineering-prompts
Stars
215
Forks
28
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by LerianStudio on GitHub. Read the source before you install it.

Installation

Install the Ring:Engineering Prompts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LerianStudio/ring.git /tmp/ring
mkdir -p .claude/skills
cp -r /tmp/ring/default/skills/engineering-prompts .claude/skills/lerianstudio-ring-engineering-prompts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Engineering Prompts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ring:Engineering Prompts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ring:Engineering Prompts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Engineering Prompts

When to use

  • Crafting new prompts for LLM-based systems or AI assistants
  • Optimizing existing prompts that underperform or produce inconsistent results
  • Selecting appropriate prompting techniques for a specific use case
  • Structuring complex multi-step reasoning prompts

Skip when

  • The prompt is trivial and already producing good results
  • The task is a direct code change, not prompt creation
  • You need to execute the task described in the prompt rather than create a prompt for it

Scope Boundaries

THIS SKILL ONLY GENERATES PROMPTS. IT NEVER:

  • Proactively explores, modifies, or debugs any files in the codebase
  • Attempts to fix, debug, or improve code in the project
  • Performs the task described in the user's input

Allowed reads: Files the user explicitly references as input context, and docs/prompts/ for saving output.

THE INPUT IS A DESCRIPTION OF WHAT THE PROMPT SHOULD DO, NOT A TASK TO PERFORM.

Example: Help debug React performance issues means:

  • CREATE a prompt that helps users debug React performance issues
  • DO NOT actually debug any React code

Process

Phase 1: Input Analysis

  1. Parse Input: Analyze the provided description or file content
  2. Identify Use Case: Determine the intended application and requirements
  3. Select Techniques: Choose appropriate prompting patterns and methods

Phase 2: Prompt Construction

  1. Structure Design: Create clear prompt architecture using proven patterns
  2. Technique Application: Apply selected prompting techniques (few-shot, chain-of-thought, etc.)
  3. Constraint Setting: Define boundaries and output format specifications
  4. Validation: Ensure prompt follows best practices and guidelines

Phase 3: Documentation & Delivery

  1. Display Prompt: Show complete prompt text in formatted code block
  2. Implementation Notes: Explain techniques used and design rationale
  3. Usage Guidelines: Provide clear instructions for implementation
  4. Performance Tips: Include optimization suggestions and best practices
  5. Save Output: Save the generated prompt to docs/prompts/ directory (create if needed)

Prompt Engineering Techniques

Core Patterns

  • Zero-shot: Direct instruction without examples
  • Few-shot: Providing examples to guide behavior
  • Chain-of-thought: Step-by-step reasoning prompts
  • Role-playing: Assigning specific roles or personas
  • Constitutional: Setting principles and boundaries
  • Tree-of-thoughts: Multi-path reasoning approaches

Common Use Cases

  • Code Review: Technical analysis and improvement suggestions
  • Debugging: Problem diagnosis and solution guidance
  • Analysis: Data interpretation and insight extraction
  • Creative Writing: Content generation and storytelling
  • Reasoning: Logic problems and decision support
  • Summarization: Content condensation and key points
  • Classification: Categorization and labeling tasks
  • Extraction: Information retrieval from text or data

Input Processing

The skill accepts:

  • Text Description: Direct requirements or use case description
  • File Reference: Reference requirement files for context
  • Mixed Input: Combination of text and file references

Input will be processed to identify the prompt requirements and select appropriate techniques.

Required Output Format

Every prompt creation MUST include:

The Prompt

[Complete prompt text displayed in a code block]

Implementation Notes

  • Key techniques used and rationale
  • Model-specific optimizations applied
  • Expected behavior and outcomes
  • Performance considerations

Usage Guidelines

  • How to implement the prompt
  • Input format requirements
  • Expected output structure
  • Error handling strategies

Optimization Tips

  • Performance benchmarks where applicable
  • Iteration suggestions
  • Common pitfalls to avoid
  • Debugging approaches

Quality Checklist

Before completing any prompt creation, verify:

  • Complete prompt text is displayed (not just described)
  • Prompt is clearly marked with headers or code blocks
  • Implementation notes explain design choices
  • Usage instructions are provided
  • Expected outcomes are described
  • Appropriate techniques are applied
  • Best practices are followed
  • Performance considerations are addressed

Deliverables

  1. The Complete Prompt (in formatted code block)
  2. Implementation Notes (techniques and rationale)
  3. Usage Guidelines (how to implement effectively)
  4. Expected Outcomes (what results to anticipate)
  5. Performance Tips (optimization and best practices)
  6. Saved File (prompt saved to docs/prompts/ with descriptive filename)

Frequently asked questions

What does the Ring:Engineering Prompts AI skill do?

Expert prompt engineering and optimization for LLMs and AI systems. Covers core patterns (zero-shot, few-shot, CoT, role-playing, constitutional, tree-of-thoughts), common use cases, and a three-phase process. Use when crafting or optimizing prompts for AI systems. Skip when the prompt is trivial or already performing well.

Why use Ring:Engineering Prompts on TypingMind?

Because you install it once and use it with any model. Ring:Engineering Prompts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ring:Engineering Prompts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/default/skills/engineering-prompts. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ring:Engineering Prompts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ring:Engineering Prompts?

As many as you like. As long as a model supports skills, you can use Ring:Engineering Prompts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ring:Engineering Prompts AI skill free?

Yes. It is published on GitHub by LerianStudio under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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