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Maximize information density: preserve all instructions, remove prose filler.
Passed 2 of 10 test cases with the skill, and 2 of 10 without it.
3 fixed1 passed either way14 still failing4 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Process the text file `docs/setup.txt` to remove redundant fluff.
| Checked | Without skill | With skill |
|---|---|---|
| Skips docs/setup.txt with a warning indicating non-markdown file type. | Missed | Passed |
The instructions your model reads when the skill loads.
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):
test cases passed, vs 2 / 10 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.