notque/condense
Maximize information density: preserve all instructions, remove prose filler.
npx skills add https://github.com/notque/vexjoy-agent --skill condense
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.
Take notque/condense from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.