| 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
| Column | Example | Notes |
|---|
| Claim Number | CN202504990 | Row key; blank / "Total" rows are dropped |
| Claimant Name | David King | |
| Location | 9607 Maple Dr, Bellevue, WA 98004 | City/State/ZIP parsed out |
| Damage Type | Mold damage - moderate severity; Water damage | Split into primary / severity / other |
| Status | Open - Claim is under investigation | Short status kept for grouping |
| Date Filed | 2026-04-11 | ISO or common date formats |
| Estimated Cost | 10608 | Numeric; missing values are counted, never invented |
See references/schema.md for full parsing rules and how
to add a column.
Workflow
- 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.
- Build the report into scratch space (never write to
output/ directly):
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.
- 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")
- 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.
- 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.
