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AbsolutelySkilled
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prompt-engineering

Craft system prompts, chain prompts, and RAG pipelines for AI agents.

207|31|Updated Mar 14, 2026
Search Tags:#chain-of-thought#ai#llm#rag#prompts#few-shot
One-click install
npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill prompt-engineering-absolutelyskilled
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/AbsolutelySkilled/AbsolutelySkilled/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill prompt-engineering-absolutelyskilled

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Prompt engineering solves the challenge of designing prompts that yield reliable, high-quality outputs from LLMs by reducing hallucinations and guiding the model toward desired behaviors.

Core Features & Use Cases

  • Structured prompt templates for system, user, and assistant roles
  • Techniques including zero-shot/few-shot prompts, chain-of-thought, RAG, and prompt chaining
  • Use cases across AI agent design, software engineering, data analytics, and content generation

Quick Start

Design a system prompt that defines roles and constraints, then request a fixed JSON output for downstream parsing.

Frequently Asked Questions about prompt-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write system prompts that enforce deterministic JSON outputs for downstream parsing?▼

System prompts enforce deterministic JSON outputs by defining strict roles, constraints, and structured formatting. Apply temperature control and output validation within your system prompt to guide the LLM toward generating reliable, parsable data.

What is the best way to structure few-shot prompts for reliable AI agent behavior?▼

Few-shot prompts guide AI behavior by providing structured templates with example interactions. Using few-shot prompting within system prompts reduces hallucinations and ensures the model follows desired patterns for multi-turn interactions.

Can I use chain-of-thought and RAG pipelines across different LLM platforms like Claude Code and Gemini CLI?▼

Chain-of-thought and RAG pipelines work across Claude Code, Gemini CLI, and OpenAI Codex. These platforms support knowledge-grounded workflows, allowing you to implement prompt chaining and retrieval-augmented generation for structured outputs.

How does prompt chaining improve LLM output reliability in software engineering tasks?▼

Prompt chaining improves reliability by breaking complex tasks into sequential, structured steps. This enforces safety checks and output validation at each stage, reducing hallucinations and ensuring deterministic behavior across multi-turn interactions.

When should I use zero-shot prompts versus few-shot templates for AI agents?▼

Use zero-shot prompts for straightforward tasks requiring minimal context, and few-shot templates when the AI agent needs specific behavioral examples. Few-shot prompting provides structured guidance, reducing hallucinations in complex software engineering workflows.

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