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ai_instruction_formatting

Format LLM instruction content into pseudo-XML with snake_case tags.

38|7|Updated May 3, 2026
Search Tags:#linting#agent workflows#prompt formatting#pseudo-xml#instruction structuring#policy constraints#output_contract
One-click install
npx skills add https://github.com/theafh/ai-modules --skill ai-instruction-formatting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai_instruction_formatting
Source: https://github.com/theafh/ai-modules/tree/main/plugins/ai_dev/skills/ai_instruction_formatting
Command: npx skills add https://github.com/theafh/ai-modules --skill ai-instruction-formatting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you organize any LLM-consumed content into a consistent pseudo-XML structure so the model can reliably interpret roles, policies, inputs, and output contracts instead of relying on ambiguous prose ordering.

Core Features & Use Cases

  • Pseudo-XML tagging for instruction clarity: Wrap semantic concerns in dedicated snake_case tags like <role>, <policy>, <inputs>, and <output_contract>.
  • Works across many host artifacts: Applies to SKILL.md files, agent definitions, command/rules documents, or any instruction snippet consumed at inference time.
  • Supports multiple file shapes: Handles prose-only markdown, full XML instruction bodies, tutorials with xml fenced examples, and mixed-agent documents with multiple top-level wrappers.
  • Mechanical validation via bundled linter: Includes a deterministic linter that enforces structural correctness (naming, balancing, depth limits, and tag-structure constraints).

Quick Start

Ask an AI to format your existing system prompt or rule document into pseudo-XML using tags for role, objective, policy, inputs, steps, examples, and an output contract, then validate the result with scripts/lint_pseudo_xml.py.

Frequently Asked Questions about ai_instruction_formatting

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

FAQPage Schema
How do I format LLM system prompts into pseudo-XML for better instruction structuring?▼

Format LLM system prompts into pseudo-XML by wrapping semantic concerns in dedicated snake_case tags like <role>, <policy>, <inputs>, and <output_contract> so the model reliably interprets instructions instead of relying on ambiguous prose ordering.

What is pseudo-XML instruction structuring and when do I need it for agent workflows?▼

Pseudo-XML instruction structuring organizes inference-time artifacts like agent definitions and rule files into self-describing tags. You need it when agent workflows require consistent structure to enforce roles, constraints, and output contracts.

Does the pseudo-XML linter enforce structural constraints like nesting depth and tag balancing?▼

Yes, the bundled deterministic linter enforces structural correctness including frontmatter alignment, H1 presence, explicit open/close tags, balanced nesting with a maximum depth of five, and naming constraints to prevent structural violations.

What's the best way to structure mixed-agent documents with multiple top-level pseudo-XML wrappers?▼

Structure mixed-agent documents by applying pseudo-XML tags to handle multiple top-level wrappers, allowing diverse file shapes like prose-only markdown, full XML instruction bodies, and tutorials with fenced examples to coexist consistently.

Why does my prompt formatting fail policy constraints when using ambiguous prose ordering?▼

Prompt formatting fails policy constraints because ambiguous prose ordering prevents reliable interpretation. Using explicit pseudo-XML tags for policies and output contracts enforces mechanical rules that resolve structural violations.

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