architecting-prompts_skill

Overview

This skill applies the 2026 Complexity-Based Guidance standards to design, optimize, and audit AI prompts and multi-stage chains. It enforces Attention Management, prevents sycophancy, and uses a Markdown/XML decision matrix to choose the correct representation. Use it when you need production-grade prompt architecture, not for one-off conversational replies or simple prompt generation tasks.

How this skill works

First the skill classifies intent as Drafting, Optimizing, or Auditing. It then loads core attention and quality standards and selects a pattern using the Signal-to-Noise rule (Markdown-first; upgrade to XML/Hybrid only for large raw data, strict NEVER/MUST constraints, or internal monologue). It applies optimization workflows and runs quality gates to produce actionable, truth-focused instructions and a final checklist.

When to use it

• Designing multi-step prompt chains or system instructions
• Optimizing existing prompts for reliability and lower hallucination risk
• Auditing prompts against attention and sycophancy standards
• Preparing prompts that include constrained outputs or structured data
• Isolating complex internal reasoning that requires stepwise scaffolding

Best practices

• Default to Markdown headers for hierarchy; reserve XML tags for data isolation or thinking scaffolds
• Limit XML: fewer than 15 tags and avoid nesting
• Immediately contradict suggested flawed paths; avoid flattering/sycophantic phrasing
• Isolate examples in <example> tags and internal reasoning in <thinking> when required
• Include a short quality gate checklist with every deliverable

Example use cases

• Draft a multi-stage instruction set for an agent that must follow strict NEVER/MUST rules using XML/Markdown hybrid
• Optimize a prompt to reduce token cost while preserving clarity using Markdown-first pattern
• Audit a chain-of-thought prompt to ensure reasoning is isolated and non-sycophantic
• Convert an ambiguous single-step prompt into a structured-template prompt with explicit output schema
• Design few-shot examples isolated in <example> tags for robust structured output