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prompt-engineering · neolabhq — Claude Code skill: what it does, how to install

Guides prompt design for agents, subagents, and LLM integrations. Helpful when building production templates, chain-of-thought prompts, or agent hooks.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add NeoLabHQ/context-engineering-kit --skill prompt-engineering -a claude-code
Open on GitHub

What prompt-engineering does

This skill outlines structured prompt engineering patterns for language models. It guides the creation of system instructions and modular templates with dynamic variables. For extraction or formatting tasks, it details few-shot configurations using two to five input-output pairs.

The reference defines an instruction hierarchy starting from system context down to the output format. It also details self-verification steps for RAG pipelines and latency reduction through prefix caching.

Excessive examples easily overflow the context window. Keeping track of token budgets is necessary before moving any prompt into production.

What it helps with

  • design system prompts with explicit rules
  • implement step-by-step reasoning chains for complex tasks
  • build reusable prompt templates with dynamic variables
  • optimize response latency and token usage in production

Example prompts for the agent

  • Optimize this support ticket classification prompt by adding 3 few-shot examples
  • Help me build a system prompt for a code review agent using step-by-step reasoning

How to install prompt-engineering

1Skills CLI (Vercel)

npx skills add NeoLabHQ/context-engineering-kit --skill prompt-engineering -a claude-code

2Manually: put the files in place

~/.claude/skills/prompt-engineering/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/prompt-engineering/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about prompt-engineering

How do I install prompt-engineering in Claude Code?

Run npx skills add NeoLabHQ/context-engineering-kit --skill prompt-engineering -a claude-code in your terminal. The configuration is stored at ~/.claude/skills/prompt-engineering/SKILL.md.

What does the prompt-engineering skill do?

It provides structured patterns for working with LLMs, covering chain-of-thought reasoning, few-shot setups, reusable prompt templates, and output validation.

How many examples are recommended for few-shot prompting?

The documentation suggests using 2 to 5 input-output pairs to balance answer consistency against context window token usage.

Similar skills

All skills for Agent setup: memory, context, orchestration →

prompt-engineering by other authors comes first.

prompt-engineering giuseppe-trisciuoglio This skill helps write, debug, and test LLM prompts. Use it for few-shot examples, system prompts, reasoning workflows, and reusable templates. CX Codex +1 3,900installs prompt-engineering-patterns Seth Hobson This skill helps design, test, and refine prompts for LLM applications. Use it for reusable templates, few-shot examples, and structured responses. CX Codex 23,418installs ai-engineer sickn33 A skill that puts the agent in the role of an LLM engineer, then works from success metrics to RAG pipelines, agent orchestration and the production plumbing around them. CX Codex +1 1,300installs llm-context brave Extracts web page text, tables, and code chunks via Brave Search API for LLM grounding. Useful when building agentic RAG workflows. CX Codex +1 1,300installs ai-product sickn33 Helps plan and assess LLM features, retrieval, validated outputs, latency, and cost controls. Use it when an AI feature needs comparison against an evaluated baseline. CX Codex +1 960installs llm-application-dev MoizIbnYousaf A skill for building LLM-powered features: prompt patterns, a RAG pipeline, API calls and retry handling. CX Codex +1 462installs context-engineering-collection muratcankoylan Patterns for production agent design: context management, multi-agent systems, long-running loops. Use when building autonomous systems that need to operate reliably. CX Codex +1 4,200installs

From the same set55

reflect A skill that makes the agent reread its own answer and list the flaws before you accept the work. CX Codex +1 1,400installs agent-evaluation Framework for testing agents, prompts, and skills using multi-dimensional scoring rubrics and LLM-as-judge prompts. CX Codex +1 1,300installs memorize Transfers insights from agent reflections and critique sessions into CLAUDE.md. Keeps your context file sharp by structuring lessons instead of bloating it with raw logs. CX Codex +1 1,300installs subagent-driven-development Runs an implementation plan through fresh subagents and puts code review between tasks, so context stays clean and issues surface early. CX Codex +1 1,300installs judge-with-debate The skill runs three independent judges who argue over a solution's score until they agree. Useful when a single pass feels too thin for a real verdict. CX Codex +1 1,300installs update-docs Updates project documentation based on uncommitted git changes or the latest commit. Keeps README files, docs/, and JSDoc comments synchronized with your codebase. CX Codex +1 1,300installs root-cause-tracing A skill that walks the agent backward through the call stack until it finds the original trigger, then fixes there instead of at the crash site. CX Codex +1 1,300installs critique The critique skill runs finished work past several AI judges: they review it independently, debate their findings, and agree on a shared list of issues. CX Codex +1 1,300installs

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