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Get Started Free →CLI-first web scraping & content extraction with optional MCP server. Use when you have target URLs and need clean, selector-based outputs (html/md/txt).
.claude/skills/foryourhealth111-pixel-scrapling/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 104% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 29% | 0% |
Scrapling is a Python-based web scraping / extraction toolkit that exposes:
scrapling ...) for fetching + extracting content into filesscrapling mcp) so an agent can call structured scraping toolsThis skill is CLI-first. Prefer it when you already have URLs and need reliable, repeatable extraction (CSS selector → file).
Use scrapling when you need:
.txt / .md / .htmlplaywrightscrapling: best for “get URL → extract selector → write file” workflows; simpler, faster iterationplaywright: best for interactive UI flows (login, multi-step navigation, downloads, complex JS actions, stateful sessions)If you must navigate or click through a UI, use playwright. If you can directly fetch the target page and just need extraction, use scrapling.
scrapling is for acquisition + extraction once you already know the URL(s).A common pipeline: 1) Search → find candidate URLs 2) Scrapling → extract focused content from chosen URLs 3) LLM → summarize / transform / analyze extracted outputs
1) Python version (Scrapling requires Python >= 3.10):
powershellpython --version
2) Scrapling CLI availability:
powershellscrapling --help
Scrapling’s CLI and MCP features are enabled via extras.
Recommended (CLI + MCP + fetchers):
powershellpython -m pip install "scrapling[ai]"
If you only want CLI fetch/extract without MCP:
powershellpython -m pip install "scrapling[fetchers]"
If you use browser-based fetchers, you may need browser binaries:
powershell# Option A: via Scrapling helper (after install) scrapling install # Option B: directly via Playwright python -m playwright install
This skill ships a thin PowerShell wrapper:
C:/Users/羽裳/.codex/skills/scrapling/scripts/scrapling.ps1It checks whether scrapling exists and prints install hints if missing.
powershellscrapling extract get "https://example.com" out.md
powershellscrapling extract get "https://example.com" out.txt --css-selector "main article"
powershellscrapling extract get "https://example.com" out.html --css-selector "#content"
powershellscrapling extract fetch "https://example.com" out.md --css-selector "main"
Tip: keep outputs in files and only feed the smallest relevant snippet to the LLM.
Scrapling can run as an MCP server. This is useful when:
Start MCP server (stdio transport by default):
powershellscrapling mcp
Optional: run MCP server with HTTP transport:
powershellscrapling mcp --http --host 127.0.0.1 --port 8765
json{ "servers": { "scrapling": { "mode": "stdio", "command": "scrapling", "args": ["mcp"], "required": false, "note": "Requires: python -m pip install \"scrapling[ai]\"" } } }
playwright.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,802 | 4,956 | -27% | 1 | 1 | 0% | 1,166 | 1,352 | +16% | 0 | 0 | — |
case-02 | fail→fail | 9,878 | 2,428 | -75% | 1 | 1 | 0% | 1,968 | 1,342 | -32% | 0 | 0 | — |
case-03 | fail→pass | 7,989 | 7,172 | -10% | 1 | 1 | 0% | 1,466 | 1,995 | +36% | 0 | 0 | — |
case-04 | fail→pass | 6,400 | 2,299 | -64% | 1 | 1 | 0% | 1,095 | 1,468 | +34% | 0 | 0 | — |
case-05 | fail→pass | 8,638 | 2,246 | -74% | 1 | 1 | 0% | 1,467 | 1,475 | +1% | 0 | 0 | — |
case-06 | fail→pass | 4,185 | 1,908 | -54% | 1 | 1 | 0% | 683 | 1,391 | +104% | 0 | 0 | — |
case-07 | fail→pass | 6,291 | 1,777 | -72% | 1 | 1 | 0% | 1,055 | 1,361 | +29% | 0 | 0 | — |
case-08 | fail→pass | 9,392 | 1,878 | -80% | 1 | 1 | 0% | 1,809 | 1,414 | -22% | 0 | 0 | — |
case-09 | pass→pass | 3,935 | 1,551 | -61% | 1 | 1 | 0% | 638 | 1,307 | +105% | 0 | 0 | — |
case-10 | fail→pass | 6,752 | 1,826 | -73% | 1 | 1 | 0% | 1,347 | 1,388 | +3% | 0 | 0 | — |
case-11 | fail→pass | 7,925 | 3,560 | -55% | 1 | 1 | 0% | 1,591 | 1,644 | +3% | 0 | 0 | — |
case-12 | pass→fail | 6,502 | 5,406 | -17% | 1 | 1 | 0% | 1,120 | 1,991 | +78% | 0 | 0 | — |
case-13 | fail→pass | 10,634 | 3,771 | -65% | 1 | 1 | 0% | 2,018 | 1,754 | -13% | 0 | 0 | — |
case-14 | pass→pass | 3,135 | 2,372 | -24% | 1 | 1 | 0% | 610 | 1,442 | +136% | 0 | 0 | — |
case-15 | pass→pass | 12,211 | 7,307 | -40% | 1 | 1 | 0% | 2,067 | 2,397 | +16% | 0 | 0 | — |
case-16 | pass→pass | 14,869 | 9,371 | -37% | 1 | 1 | 0% | 2,353 | 2,608 | +11% | 0 | 0 | — |
case-17 | fail→pass | 9,563 | 3,086 | -68% | 1 | 1 | 0% | 1,763 | 1,647 | -7% | 0 | 0 | — |
case-18 | pass→pass | 5,135 | 2,652 | -48% | 1 | 1 | 0% | 896 | 1,486 | +66% | 0 | 0 | — |
case-19 | pass→pass | 16,101 | 15,822 | -2% | 1 | 1 | 0% | 1,911 | 3,121 | +63% | 0 | 0 | — |
case-20 | fail→pass | 11,631 | 5,103 | -56% | 1 | 1 | 0% | 1,995 | 2,070 | +4% | 0 | 0 | — |
case-21 | pass→pass | 10,658 | 6,557 | -38% | 1 | 1 | 0% | 1,795 | 2,189 | +22% | 0 | 0 | — |
case-22 | pass→pass | 14,923 | 7,378 | -51% | 1 | 1 | 0% | 2,418 | 2,323 | -4% | 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 21 counted toward the lift figure. The other 1 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 +45 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.