testcase-importerlisted

Import and normalize existing test-case data — xls/xlsx, CSV/TSV, PDF tables, or a pasted/markdown table — into this project's canonical test-case CSV and render it into the offline HTML console. Fuzzy-maps foreign headers onto the schema, standardizes Type (POS/NEG/VAPT), Outcome (TN/TP/FP/FN!) and the Priority scales, keeps source IDs (or generates them), infers missing tag fields, and flags uncertain ones for review. Use whenever someone has test cases in a sheet, CSV, PDF, or table and wants them in the project format or in the console — e.g. "convert this QA spreadsheet", "map this test matrix to our format", "normalize this testcases xlsx", "turn this PDF of test cases into the CSV", or pasting a table and asking to "add these to the matrix". Do NOT use it to author brand-new cases from a feature/flow (that's the testcase-architect agent), to import non-test-case data such as a customer or sales CSV into a database or chart, to edit/restyle the console, or to query an existing canonical CSV.
labspangaea/pangaealabs-claude-plugins-marketplace · ★ 0 · Data & Documents · score 62

Install: claude install-skill labspangaea/pangaealabs-claude-plugins-marketplace

# Test-case importer Turn messy, foreign-formatted test-case data into the project's canonical CSV (the 10 columns the HTML console reads) and render it. The hard parts — parsing odd formats, matching column names, normalizing vocab, generating IDs, validating — are done by the bundled script. The judgment part — inferring the tag fields a source didn't provide — is yours, guided by the taxonomy, because a keyword script guesses those badly and silently. The canonical schema, vocab, and the rules for inferring missing fields live in `references/schema.md`. **Read it before filling any gaps.** ## Workflow ### 1 — Locate the input A file path (`.xlsx`/`.xls`/`.csv`/`.tsv`/`.pdf`/`.md`) or a table pasted into chat. For a pasted markdown/table, save it to a `.md` file or pipe it via stdin (`... | ... -`). ### 2 — Run the normalizer ```bash python scripts/normalize_testcases.py <INPUT> --name <NAME> ``` It writes `<NAME>-testcases.csv` (canonical columns, blanks where it couldn't decide) and `<NAME>-import-gaps.json`, and prints a summary: rows parsed, which columns mapped, which source columns were **dropped** (unmapped), and which fields still need work. - PDF needs `pdfplumber` (`pip install pdfplumber`); if absent the script says so — ask the user to install it or export the table to CSV/XLSX. - Old `.xls` uses `xlrd`; `.xlsx` uses `openpyxl`/`pandas` (already available). ### 3 — Read the gaps report and the dropped columns Open `<NAME>-import-gaps.json`. Two things m