skills/code-quality/condense/SKILL.md
Maximize information density: preserve all instructions, remove prose filler.
npx skillsauth add notque/claude-code-toolkit condenseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Strip prose filler from .md files. Preserve every instruction. This skill practices what it preaches.
Identify targets.
agents/*.md). Expand, list matches, confirm with user.Mechanical pre-pass (deterministic, run before LLM condensing): strip trailing whitespace and consecutive blank lines that inflate Opus token counts. The script handles the mechanical reduction so the LLM phase focuses on prose density.
python3 scripts/check-whitespace.py --fix <target-file-or-dir> # 0=clean, 1=violations fixed
Run on the scoped targets (defaults to agents/**/*.md and skills/**/*.md when no path given). Then proceed to the LLM pass on the same files.
Gate: At least one target file identified and readable; mechanical pre-pass run.
For each file:
KEEP (never cut):
CUT:
STYLE: Short sentences. Active voice. Concrete words. If you can cut a word without losing an instruction, cut it.
Before cutting any sentence: "If I remove this, does the reader lose an instruction, rule, or decision?" No = cut. Yes = keep.
Do not reorganize sections, change meaning, add ideas, alter paths/commands, drop tables or code blocks, or modify YAML frontmatter values.
For each condensed file:
python3 -c "import yaml; yaml.safe_load(open('<file>').read().split('---')[1])"
| File | Before | After | Reduction | table with word counts.Gate: YAML parses. No instructions lost. Reduction reported.
No prose to cut: Report 0% reduction, move to next file.
Instruction removed: Re-read original, restore missing instruction, re-verify.
YAML broken: Restore original frontmatter verbatim, re-condense body only.
Non-.md file: Skip with warning.
documentation
Document translation: quick/normal/refined modes with chunked parallel subagents and glossary support.
development
AI image generation: Gemini and Nano Banana backends; single/series/batch workflows with prompt-to-disk.
testing
Unified voice content generation pipeline with mandatory validation and joy-check. 13-phase pipeline: LOAD, GROUND, STATS-CHECKPOINT, GENERATE, HOOK-GATE, VALIDATE, REFINE, VARIETY-GATE, JOY-CHECK, ANTI-AI, CLOSE-GATE, OUTPUT, CLEANUP. Use when writing articles, blog posts, or any content that uses a voice profile. Use for "write article", "blog post", "write in voice", "generate content", "draft article", "write about".
documentation
Critique-and-rewrite loop for voice fidelity validation.