Snapgrab — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Snapgrab (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
한국어 문서 · llms.txt
URL to screenshot with metadata. Claude Vision optimized.
from snapgrab import capture
result = await capture("https://example.com")
print(result.path) # /tmp/snapgrab/example_com_desktop_20260317_120000.png
print(result.metadata.title) # "Example Domain"
print(result.vision_tokens) # ~2764pip install snapgrabFirst run will prompt to install Playwright browsers:
playwright install chromiumimport asyncio
from snapgrab import capture
async def main():
# Basic screenshot
result = await capture("https://example.com")
# Mobile viewport, full page
result = await capture("https://example.com", viewport="mobile", full_page=True)
# Dark mode, JPEG format
result = await capture("https://example.com", dark_mode=True, format="jpeg")
# Specific element
result = await capture("https://example.com", selector="#main-content")
# Custom viewport
result = await capture("https://example.com", viewport=(1440, 900))
asyncio.run(main())snapgrab https://example.com # basic PNG
snapgrab https://example.com -v mobile -f # mobile, full page
snapgrab https://example.com --format jpeg -q 90 # JPEG quality 90
snapgrab https://example.com -s "#hero" --dark-mode # element + dark mode
snapgrab https://example.com -j # JSON output
snapgrab meta https://example.com # metadata onlypip install "snapgrab[mcp]"
snapgrab-mcp # starts stdio MCP serverTools:
capture_screenshot — capture URL with metadata and vision token estimatecapture_comparison — compare desktop vs mobile (or any viewports)extract_page_metadata — metadata only, no screenshotflowchart LR
A["🔗 URL"] --> B["Playwright\nlaunch browser"]
B --> C["📸 Screenshot\nfull page / viewport"]
C --> D["Resize for\nClaude Vision"]
D --> E["✅ Image +\nMetadata"]result.path # saved file path
result.format # "png", "jpeg", "pdf"
result.width # viewport width
result.height # page height (full_page) or viewport height
result.file_size # bytes
result.vision_tokens # estimated Claude Vision token cost
result.vision_path # path to Vision-optimized image (≤1568px)
result.processing_time_ms
result.metadata.title
result.metadata.description
result.metadata.og_title
result.metadata.og_image
result.metadata.favicon_url
result.metadata.status_code
result.metadata.url # final URL after redirects<!-- mcp-name: io.github.QuartzUnit/snapgrab -->
<sub>Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.</sub>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.