Condense Markdown files by removing prose filler while preserving instructions and structure.
npx skills add https://github.com/notque/vexjoy-agent --skill condense-notque
Please help me install this Agent Skill. Skill: condense Source: https://github.com/notque/vexjoy-agent/tree/main/skills/code-quality/condense Command: npx skills add https://github.com/notque/vexjoy-agent --skill condense-notque
Reduces bloated Markdown files by removing prose filler while preserving every instruction, rule, and gate so the edited docs remain operationally correct.
Run /condense <file> to condense a targeted Markdown file while preserving all instructions and structure.
High-intent search queries and answers about installing and using this skill.
Condense Markdown files by running a mechanical whitespace pre-pass followed by in-place rewriting that removes prose filler while preserving YAML frontmatter, code blocks, tables, and all operational instructions.
Batch condensing supports glob patterns and larger file sets, applying deterministic whitespace removal and verification gates across all targeted Markdown files to ensure key terms and instructions remain present.
Markdown condensing preserves YAML frontmatter exactly as written and validates it through a verification gate that confirms the YAML still parses correctly after all prose filler is removed.
Removing filler text for faster agent use requires a deterministic pre-pass to eliminate trailing whitespace and consecutive blank lines, then in-place rewriting that maximizes information density without altering phase structure or deleting gates.
Condensing documentation requires Python 3 along with yaml and grep dependencies to execute the pre-pass whitespace removal, in-place rewriting, and verification gates that ensure structural integrity and instruction preservation.
Markdown condensing can break code blocks and tables if filler removal is not constrained, so this approach explicitly preserves code blocks, tables, and YAML frontmatter while only targeting prose filler for deletion.
IhsanDanish25Validate trading strategy YAML files and output PASS/REVISE/REJECT verdicts.
SamuelSahaReduce AI token consumption via shallow skill loading, context pruning, and output compression.
AbeJitsuApply user-defined formatting standards across code, documentation, and content outputs using ESLint and Prettier.
svssdevaCompress natural-language memory markdown files while preserving code, URLs, and structure.
