version: "1.0.0" name: zava-claims-export description: Converts a raw claims export workbook (one claim per row) into a polished, formatted Excel report with a Dashboard (KPIs, breakdowns by status, damage type, severity, and location, plus charts) and a clean Claims Detail table. The report layout is a built-in reference template, so every run looks consistent. Use when the user asks to "turn this claims export into a report", "build a claims report", "format the claims data", "make a claims dashboard", "summarize these claims", "claims summary report", or drops a claims_export spreadsheet and wants it turned into a nice report. Also use for similar flat claim/loss/incident exports that share the columns Claim Number, Claimant, Location, Damage Type, Status, Date Filed, and Estimated Cost. Do NOT use for writing a prose narrative document (use docx), building slides (use pptx), ad-hoc one-off spreadsheet edits unrelated to claims (use xlsx), or interactive dashboards the user clicks through (use canvas). license: Proprietary. LICENSE.txt has complete terms

Claims Report

Automate the boring part of claims reporting: take a raw export and return a report a manager would be happy to open — headline numbers up top, breakdowns and charts on a Dashboard, and a tidy, filterable Claims Detail table behind it.

The report design lives in the THEME block at the top of scripts/build_report.py. That block is the reference template — colours, fonts, and number formats. Edit it once and every future report inherits it.

When to Use

Trigger when the user has a flat claims/loss/incident export (one claim per row) and wants it turned into a formatted, summarized report. The canonical input is the columns below; the script tolerates minor header-name variation.

When NOT to Use

• The user wants a written narrative (memo, summary letter) → use docx.
• The user wants slides for a meeting → use pptx.
• The user wants a clickable/interactive dashboard → use canvas.
• A generic spreadsheet task with no claims structure → use xlsx.
• The export is not row-per-claim (e.g. already a pivot/summary) — reshape

it first, or fall back to xlsx.

Expected Input Schema

See references/schema.md for full parsing rules and how to add a column.

Workflow

1. Find the input. If the user attached a file, use that path. Otherwise

look in input/ for the claims export (Glob input/**/*.xlsx). If nothing is found, ask the user for the file — do not invent data.

1. Build the report into scratch space (never write to output/ directly):

``bash python scripts/build_report.py --in "<input.xlsx>" --out working/claims_report.xlsx --title "Insurance Claims Report" ` The script prints a one-line summary (claims=… skipped_rows=… total_cost=… avg_cost=…`) and notes any claims missing a cost. Read that line back to the user in plain language.

1. Publish to the user surface with the artifact tool (output/ is

read-only to direct writes): `` CopyArtifact(surface="output", source="working/claims_report.xlsx", destination="Claims Report.xlsx") ``

1. Confirm delivery (blocking): Glob output/**/* must show the file

before you tell the user it's ready. If it's missing, re-run step 3.

1. Summarize the headline numbers (total claims, total & average estimated

cost, open vs closed, top damage type) so the user gets value in the chat even before opening the file. Ground every number in the script's output — never estimate.

Output

A single .xlsx with two sheets:

• Dashboard — title banner (source file, generated date, filing range),

six KPI cards (Total Claims, Total / Average Est. Cost, Open, Closed, Severe-Severity), breakdown tables by Status, Damage Type, Severity, State, and City (each with count, % of claims, and est. cost + a totals row), and two bar charts (claims by status, cost by damage type).

• Claims Detail — the cleaned claim table: Claim Number, Claimant, City,

State, Damage Type, Severity (colour-cued), Other Damage, Status, Date Filed, and Est. Cost (currency-formatted), with banded rows, a frozen header, an AutoFilter, and a grand-total row.

Guardrails

• Never fabricate data. Only values present in the source appear in the

report. Blank or "Total" rows are dropped and counted; missing costs are reported, not filled in.

• Reconcile before reporting. The detail total and the dashboard totals both

derive from the same cleaned rows; if the printed total_cost looks wrong, re-check the input rather than the report.

• Currency is display-only — the raw exports carry no currency code; the

$ format is cosmetic. If the user says the data isn't USD, change THEME["money_fmt"] in the script.

• Don't hand-edit the output for one-off fixes — change the input or the

THEME/logic and re-run, so the report stays reproducible.

• Large files: the script streams values with openpyxl; for very large

exports (tens of thousands of rows) warn the user it may take a moment.