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Get Started Free →Maximize information density: preserve all instructions, remove prose filler.
.claude/skills/notque-condense/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 710% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 165% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 633% | 0% |
| case-05 | ✓→✗ | ▼ Worse | -21% | 0% |
| case-06 | ✓→✗ | ▼ Worse | -74% | 0% |
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.
bashpython3 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:
bash 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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,911 | 5,261 | -59% | 1 | 1 | 0% | 2,460 | 996 | -60% | 0 | 0 | — |
case-02 | fail→fail | 2,622 | 2,648 | +1% | 1 | 1 | 0% | 303 | 978 | +223% | 0 | 0 | — |
case-03 | fail→fail | 4,117 | 4,120 | +0% | 1 | 1 | 0% | 176 | 964 | +448% | 0 | 0 | — |
case-04 | fail→fail | 11,328 | 5,028 | -56% | 1 | 1 | 0% | 1,743 | 1,032 | -41% | 0 | 0 | — |
case-05 | pass→fail | 8,385 | 2,143 | -74% | 1 | 1 | 0% | 1,191 | 937 | -21% | 0 | 0 | — |
case-06 | pass→fail | 17,603 | 4,374 | -75% | 1 | 1 | 0% | 3,469 | 905 | -74% | 0 | 0 | — |
case-21 | fail→fail | 10,917 | 5,683 | -48% | 1 | 1 | 0% | 1,720 | 987 | -43% | 0 | 0 | — |
case-07 | fail→fail | 7,416 | 3,087 | -58% | 1 | 1 | 0% | 1,038 | 1,023 | -1% | 0 | 0 | — |
case-08 | fail→fail | 5,684 | 5,716 | +1% | 1 | 1 | 0% | 329 | 1,049 | +219% | 0 | 0 | — |
case-09 | fail→fail | 9,003 | 11,886 | +32% | 1 | 1 | 0% | 1,299 | 2,571 | +98% | 0 | 0 | — |
case-10 | fail→pass | 3,060 | 17,996 | +488% | 1 | 1 | 0% | 472 | 3,823 | +710% | 0 | 0 | — |
case-11 | fail→fail | 7,808 | 32,829 | +320% | 1 | 1 | 0% | 1,191 | 5,619 | +372% | 0 | 0 | — |
case-12 | pass→fail | 10,634 | 5,700 | -46% | 1 | 1 | 0% | 1,570 | 963 | -39% | 0 | 0 | — |
case-13 | fail→fail | 3,960 | 5,918 | +49% | 1 | 1 | 0% | 539 | 920 | +71% | 0 | 0 | — |
case-14 | pass→fail | 14,264 | 6,182 | -57% | 1 | 1 | 0% | 2,116 | 1,170 | -45% | 0 | 0 | — |
case-15 | pass→pass | 9,651 | 4,637 | -52% | 1 | 1 | 0% | 1,430 | 1,281 | -10% | 0 | 0 | — |
case-16 | fail→pass | 9,589 | 6,750 | -30% | 1 | 1 | 0% | 679 | 1,801 | +165% | 0 | 0 | — |
case-17 | fail→fail | 6,898 | 3,385 | -51% | 1 | 1 | 0% | 1,137 | 1,306 | +15% | 0 | 0 | — |
case-18 | fail→pass | 3,536 | 16,423 | +364% | 1 | 1 | 0% | 371 | 2,720 | +633% | 0 | 0 | — |
case-19 | fail→fail | 7,632 | 6,522 | -15% | 1 | 1 | 0% | 1,101 | 1,150 | +4% | 0 | 0 | — |
case-20 | fail→fail | 7,196 | 6,654 | -8% | 1 | 1 | 0% | 1,137 | 1,083 | -5% | 0 | 0 | — |
case-22 | fail→fail | 7,083 | 6,698 | -5% | 1 | 1 | 0% | 1,086 | 1,069 | -2% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 10 counted toward the lift figure. The other 12 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -5 percentage points is the difference between those two pass rates over the 10 comparable cases. 5 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.