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Get Started Free →Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.
.claude/skills/sickn33-defuddle/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -72% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -70% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -62% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -56% | 0% |
Use Defuddle CLI to extract clean readable content from web pages. Prefer over WebFetch for standard web pages — it removes navigation, ads, and clutter, reducing token usage.
If not installed: npm install -g defuddle
Always use --md for markdown output:
bashdefuddle parse <url> --md
Save to file:
bashdefuddle parse <url> --md -o content.md
Extract specific metadata:
bashdefuddle parse <url> -p title defuddle parse <url> -p description defuddle parse <url> -p domain
| Flag | Format | |------|--------| | --md | Markdown (default choice) | | --json | JSON with both HTML and markdown | | (none) | HTML | | -p <name> | Specific metadata property |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 8,587 | 4,991 | -42% | 1 | 1 | 0% | 1,077 | 630 | -42% | 0 | 0 | — |
case-02 | fail→pass | 2,691 | 5,252 | +95% | 1 | 1 | 0% | 496 | 521 | +5% | 0 | 0 | — |
case-03 | fail→fail | 3,054 | 3,126 | +2% | 1 | 1 | 0% | 513 | 494 | -4% | 0 | 0 | — |
case-20 | pass→pass | 12,746 | 4,503 | -65% | 1 | 1 | 0% | 2,327 | 1,172 | -50% | 0 | 0 | — |
case-04 | fail→pass | 12,580 | 1,668 | -87% | 1 | 1 | 0% | 2,368 | 654 | -72% | 0 | 0 | — |
case-05 | fail→pass | 9,121 | 1,327 | -85% | 1 | 1 | 0% | 1,920 | 578 | -70% | 0 | 0 | — |
case-06 | fail→pass | 9,313 | 1,902 | -80% | 1 | 1 | 0% | 1,853 | 708 | -62% | 0 | 0 | — |
case-07 | fail→pass | 8,063 | 2,200 | -73% | 1 | 1 | 0% | 1,588 | 699 | -56% | 0 | 0 | — |
case-08 | fail→pass | 10,796 | 2,354 | -78% | 1 | 1 | 0% | 1,970 | 811 | -59% | 0 | 0 | — |
case-09 | fail→pass | 12,444 | 1,785 | -86% | 1 | 1 | 0% | 2,522 | 686 | -73% | 0 | 0 | — |
case-10 | fail→pass | 10,642 | 2,905 | -73% | 1 | 1 | 0% | 2,075 | 864 | -58% | 0 | 0 | — |
case-11 | fail→pass | 6,847 | 1,503 | -78% | 1 | 1 | 0% | 1,393 | 568 | -59% | 0 | 0 | — |
case-12 | fail→pass | 6,123 | 1,162 | -81% | 1 | 1 | 0% | 1,281 | 486 | -62% | 0 | 0 | — |
case-13 | fail→pass | 10,581 | 3,688 | -65% | 1 | 1 | 0% | 1,834 | 995 | -46% | 0 | 0 | — |
case-14 | fail→pass | 9,957 | 2,751 | -72% | 1 | 1 | 0% | 1,939 | 872 | -55% | 0 | 0 | — |
case-15 | fail→pass | 11,043 | 1,485 | -87% | 1 | 1 | 0% | 1,972 | 542 | -73% | 0 | 0 | — |
case-16 | fail→pass | 10,307 | 1,293 | -87% | 1 | 1 | 0% | 1,954 | 583 | -70% | 0 | 0 | — |
case-17 | fail→pass | 7,740 | 1,566 | -80% | 1 | 1 | 0% | 1,584 | 547 | -65% | 0 | 0 | — |
case-18 | fail→pass | 7,197 | 1,304 | -82% | 1 | 1 | 0% | 1,234 | 573 | -54% | 0 | 0 | — |
case-19 | fail→pass | 8,008 | 4,730 | -41% | 1 | 1 | 0% | 1,581 | 1,154 | -27% | 0 | 0 | — |
case-21 | pass→pass | 7,156 | 3,802 | -47% | 1 | 1 | 0% | 1,476 | 1,098 | -26% | 0 | 0 | — |
case-22 | pass→pass | 6,833 | 4,481 | -34% | 1 | 1 | 0% | 1,459 | 1,202 | -18% | 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 20 counted toward the lift figure. The other 2 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 +77 percentage points is the difference between those two pass rates over the 20 comparable cases.
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.