ai-prompting

LLM application patterns, prompt optimization techniques, and AI-powered data analysis workflows.

5 stars

byvamseeachanta

View on GitHub Installation ↓

Best use case

ai-prompting is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

LLM application patterns, prompt optimization techniques, and AI-powered data analysis workflows.

Teams using ai-prompting should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/ai-prompting/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/ai/prompting/ai-prompting/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/ai-prompting/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How ai-prompting Compares

Feature / Agent	ai-prompting	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

LLM application patterns, prompt optimization techniques, and AI-powered data analysis workflows.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Ai Prompting

## Overview

This library contains 5 production-ready skills for building LLM-powered applications, optimizing prompts, and integrating AI into data workflows. Each skill covers a specific aspect of AI application development with patterns for reliability, evaluation, and scaling. Skills follow the Anthropic Skills format with practical examples from real-world AI implementations.

## Quick Start

```bash
# Browse available skills
ls skills/ai-prompting/

# Read a skill
cat skills/ai-prompting/langchain/SKILL.md

# Skills are documentation - implement patterns in your AI applications
```

## Version History

- **1.0.0** (2026-01-17): Initial release with 5 AI prompting skills

---

*These skills represent patterns refined across production LLM applications processing millions of queries with optimized cost and quality.*

## Sub-Skills

- [1. Version Control Prompts (+3)](1-version-control-prompts/SKILL.md)

## Sub-Skills

- [Available Skills](available-skills/SKILL.md)
- [Application Frameworks (+2)](application-frameworks/SKILL.md)
- [Choose langchain when: (+4)](choose-langchain-when/SKILL.md)
- [LangChain RAG Pipeline (+4)](langchain-rag-pipeline/SKILL.md)
- [RAG Architecture (+2)](rag-architecture/SKILL.md)
- [Structured Output (+2)](structured-output/SKILL.md)
- [Integration with Workspace-Hub](integration-with-workspace-hub/SKILL.md)
- [Testing AI Applications](testing-ai-applications/SKILL.md)
- [Related Resources](related-resources/SKILL.md)