.claude/skills/normalize-whitespaceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Normalize indentation of a large reference text document (e.g. README.md) to a consistent TAB style while keeping content byte-identical. Use when asked to fix inconsistent leading spaces, align command blocks, or clean indentation spread across hundreds of lines in a prose-heavy, single-fence document. Built from the 2026-09 instguid README.md normalization session.
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These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/normalize-whitespaceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/normalize-whitespaceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/normalize-whitespaceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @j3ffyang/normalize-whitespaceclawhub inspect @j3ffyang/normalize-whitespace --version 1.0.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
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sha256:4ad8e44b9e6b988e372cb2f2d834e73802ee5b9f34958b27924dfc1e4625f591SKILL.md- Initial release of `normalize-whitespace` for normalizing leading whitespace in large text documents to a consistent tab indentation style. - Ensures content remains byte-identical, only altering leading/trailing whitespace, with strict integrity checks (`git diff --ignore-all-space --ignore-blank-lines`). - Provides detailed rules and a step-by-step Python-based procedure for classifying, scanning, and normalizing whitespace. - Explicit listing of "leave-as-is" classes to protect verbatim blocks, ASCII art, and other sensitive content from modification. - Includes robust verification steps to prevent unintentional content changes during normalization.