Incident Report Scraper — Testing skill for Claude Code
Windows desktop tool that scrapes every incident from an Angular support portal with Playwright, keeps a change-tracked store, and builds an Excel workbook plus a self-contained offline HTML dashboard.
How to install Incident Report Scraper
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open Ark310/incident-report-scraper and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Incident Report Scraper does
Windows desktop tool that scrapes every incident from an Angular support portal with Playwright, keeps a change-tracked store, and builds an Excel workbook plus a self-contained offline HTML dashboard with trends, leaderboards, backlog and VADER sentiment. PySide6 GUI, PyInstaller exe, 204 tests. Built with Claude Code.
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README
Incident Report Scraper
A Windows desktop tool that scrapes every incident report from a web support portal into a living, change-tracked data store, then turns it into a shareable Excel workbook and a self-contained offline HTML dashboard.
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🧭 Part of **Abdul Raqeeb Khatri's portfolio**: [📂 Hub](https://github.com/Ark310/portfolio) · [🌐 Site](https://ark310.github.io) · [💼 Experience](https://github.com/Ark310/experience)
Overview
The support team logs quality incidents (policy breaches, QA issues, missed follow-ups) in an internal Angular support portal. The portal can list them, but it cannot answer the questions a team lead actually asks: *Which categories keep coming back? Who opens most incidents? Who gets marked at fault, and who consistently resolves things well? How big is the open backlog and how old is it?*
Incident Report Scraper answers those questions in one click. It logs into the portal, discovers every incident (about 340 at the time of writing), scrapes each detail page concurrently, and keeps an incremental store that detects genuine field changes between runs. From that store it builds:
incidents.xlsx— the living data document: raw incidents, normalized fault details, narrative fields, trends, weekl
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