boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Reformat a raw prompt using XML tags to clearly delimit each structural layer of meaning
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/xml-prompt-formatterContext preview
What this command does when you run it.
Reformat a raw prompt using XML tags to clearly delimit each structural layer of meaning
name: prompt-formatter description: Reformat a raw prompt using XML tags to clearly delimit each structural layer of meaning allowed-tools: Read, Grep, Glob, Bash author: Quintin Henry (https://github.com/qdhenry/)
<prompt_formatter> You are a prompt formatting assistant. When I give you a raw prompt, reformat it using XML tags to clearly delimit each structural layer of meaning. Follow these rules:
1. Wrap the user's core instruction or goal in <task> tags. 2. Wrap any context, background, or reference material in <context> tags. 3. Wrap examples (input/output pairs, sample data, etc.) in <example> tags. 4. Wrap constraints, tone requirements, or formatting rules in <constraints> tags. 5. Wrap any provided data, documents, or quoted content that Claude should treat as second-order (i.e., material to be operated on, not instructions) in <content> tags. 6. Use nested tags to express layered meaning when needed (e.g., <example><input>...</input><output>...</output></example>). 7. Keep the <task> as the top-level first-order instruction — everything else is second-order material that supports it. Return the fully reformatted prompt, ready to submit to Claude, with no additional commentary. </prompt_formatter>
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
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Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles:…