prompt-craft

Design and repair LLM prompts with instruction hierarchy and output contracts.

1|Updated May 6, 2026
One-click install
npx skills add https://github.com/jacob-balslev/skill-graph --skill prompt-craft-jacob-balslev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-craft
Source: https://github.com/jacob-balslev/skill-graph/tree/main/marketplace/skills/prompt-craft
Command: npx skills add https://github.com/jacob-balslev/skill-graph --skill prompt-craft-jacob-balslev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompt-craft helps you turn vague or brittle prompt instructions into a dependable, inspectable prompt structure that reliably produces the right output format and behavior under realistic inputs.

Core Features & Use Cases

  • Instruction hierarchy and message roles: place stable policy and rules above user-controlled content to reduce instruction-confusion failures.
  • Prompt anatomy and structure: design task statements, context boundaries, procedures, examples, and failure paths so the model knows exactly what to do.
  • Output-format discipline: specify a clear output contract (including structured output expectations) and define verification, retries, and fallback behavior.
  • Few-shot and boundary cases: use counterexamples and edge cases to prevent the prompt from teaching the wrong pattern.
  • Prompt-injection resistance guidance: separate instructions from data and describe how to respond to attempted overrides or prompt extraction.
  • Eval-driven iteration: revise one prompt surface at a time and keep changes only when they improve measured outcomes.

Quick Start

Use prompt-craft to rewrite your agent prompt so it enforces a strict instruction hierarchy, a clear output contract, and a safe retry/fallback path for invalid or missing information.

Frequently Asked Questions about prompt-craft

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

FAQPage Schema
How do I design LLM prompts that produce consistent structured output?▼

To design LLM prompts for consistent structured output, define a clear output contract with validation and retry/fallback logic, separating stable instructions from user data to ensure reliable formatting.

What is the best way to prevent prompt injection in agent workflows?▼

Preventing prompt injection in agent workflows requires enforcing an instruction hierarchy that places stable policy above user content, and explicitly separating instructions from untrusted data boundaries.

How do I use few-shot examples to prevent wrong output patterns in prompt engineering?▼

Use few-shot examples in prompt engineering by including counterexamples and boundary cases, which prevents the model from learning the wrong pattern when processing edge cases or ambiguous inputs.

Can I harden existing agent instructions against instruction-confusion failures?▼

Yes, you can harden existing agent instructions against instruction-confusion failures by restructuring the prompt anatomy to enforce strict instruction hierarchy and defining clear context boundaries.

Why does my prompt output format break on edge cases and how do I fix it?▼

Prompt output formats break on edge cases due to missing boundary examples and undefined failure paths; fix this by adding eval-driven iteration against a stable evaluation set and specifying verification steps.