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prompt-engineering

Design and optimize prompts for clear, structured, token-efficient Claude outputs.

Updated Feb 16, 2026
Search Tags:#prompt engineering#prompt optimization#claude#token efficiency#few-shot#output format
One-click install
npx skills add https://github.com/Fivents/one-id --skill prompt-engineering-fivents
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/Fivents/one-id/tree/main/.claude/skills/prompt-engineering
Command: npx skills add https://github.com/Fivents/one-id --skill prompt-engineering-fivents

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses unclear, inconsistent, or token-inefficient prompts that produce unreliable outputs from Claude models by providing structured guidance and best practices to craft precise instructions.

Core Features & Use Cases

  • Structured Prompt Templates: Prescribes context, instructions, constraints, and examples to produce repeatable results.
  • Output Format Enforcement: Guides strict format specifications (JSON, SQL, YAML, etc.) to enable downstream parsing and automation.
  • Token Efficiency & Robustness: Recommends minimizing unnecessary context, using few-shot examples, assigning roles, and including edge-case handling for production-ready prompts.
  • Use Cases: Designing API prompts for data extraction, refining automation workflows, and converting vague human requests into strict machine-readable instructions.

Quick Start

Create a production-ready Claude prompt that extracts invoice fields into a strict JSON schema with context, explicit instructions, constraints, and a short example.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I optimize prompts for Claude to output strict JSON schemas?▼

To optimize prompts for Claude, enforce explicit output format instructions, assign clear roles, provide few-shot examples, and define strict constraints to ensure predictable, machine-readable JSON outputs.

Why does my Claude prompt produce inconsistent or token-inefficient outputs?▼

Claude prompts produce inconsistent outputs when lacking structured context, explicit instructions, and edge-case handling. Token efficiency drops without minimizing unnecessary context and using targeted few-shot examples.

What is the best way to structure a Claude prompt for API automation workflows?▼

The best way to structure a Claude prompt for API automation is to prescribe context, instructions, and constraints, paired with few-shot examples to produce repeatable, predictable machine-readable results.

How do I use few-shot examples to improve Claude output format predictability?▼

Use few-shot examples within your Claude prompt to demonstrate the exact output format, guiding the model to replicate the structure and minimizing token usage for production-ready automation.

Can I convert vague human requests into strict machine-readable instructions for Claude?▼

Yes, you can convert vague requests into strict machine-readable instructions by applying structured prompt templates that enforce explicit formatting, role assignment, and edge-case handling for Claude.

When should I include edge-case handling in my Claude prompt engineering?▼

Include edge-case handling in your Claude prompt engineering when designing production-ready API integrations or automation workflows that require predictable behavior and strict output formats under varying inputs.

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