estimate-delta-annotator

name

estimate-delta-annotator

description

Compares two collision-repair estimates (shop/preliminary vs. insurer supplement/SOR) and produces one annotated PDF of the lower-cost estimate that bridges the two appraisals. Cost pass strikes under-valued prices, labor hours, and rates and stamps the higher estimate's value (red/yellow), underlines matches, and notes missing items. OEM pass flags aftermarket/used parts, skipped scans/calibrations, one-time-use reuse, and warranty/safety language that contradict OEM position statements (blue/cyan), citing the statement and jurisdictional law. Use whenever the user wants to compare, reconcile, or annotate two estimates, build a delta / cost-gap or OEM citation density report, mark up an SOR or supplement, check OEM position statements, or justify a supplement. Triggers: delta, differences, cost gap, supplement review, reconcile estimates, annotate estimate, OEM position statement, aftermarket vs OEM, ADAS calibration, jurisdictional law, bridge the appraisal gap.

Estimate Delta Annotator

Body shops and insurers rarely agree line-for-line. Given two estimates for the same repair — a shop/preliminary estimate and an insurer supplement (SOR / Supplement of Record) — this skill marks up the lower-cost one so a reviewer sees, at a glance, where it falls short and why. Output is a single annotated PDF carrying two categories of markup:

• Cost gaps (red / yellow) — under-valued prices, labor hours, and rates, plus items missing from the lower estimate.
• OEM / warranty / safety flags (blue / cyan) — aftermarket or used parts, skipped scans/calibrations, reused one-time parts, and disclosure language that contradicts OEM position statements.

Scripts

• scripts/extract_estimate.py "<pdf>" --out x.json — structured line items and ESTIMATE TOTALS with coordinates, plus the parsed grand total.
• scripts/annotate_pdf.py "<target>.pdf" instructions.json "<out>.pdf" — applies the markup. Annotation types: underline, replace, highlight, note; each takes optional color (red|blue) and hl (yellow|cyan).

Workflow

1. Extract both estimates

python scripts/extract_estimate.py "<estimate A>.pdf" --out /tmp/a.json
python scripts/extract_estimate.py "<estimate B>.pdf" --out /tmp/b.json

Line records carry price_bbox and row_top; totals records carry hrs_bbox and rate_bbox. Pass these coordinates straight to the annotator — never guess them.

2. Pick the target (auto-detect by total)

The higher-grand_total estimate is the source of the values you stamp; the lower-grand_total estimate is the target you annotate. This holds even when the lower estimate has more lines (a supplement can carry extra mechanical work yet still total less). Tell the user which file you chose and cite both totals. If totals tie or one is null, ask which file is the shop estimate.

3. COST pass — line items

For each priced line on the target, find its counterpart on the source. Match on part number first (strongest signal), then normalized description + operation + section. Respect side/qualifier tokens (LT vs RT, "w/o GLE63"). The two estimates use different software, so line order and numbers won't correspond — match on meaning. Some target lines have no source counterpart (extra supplement work); leave those alone.

• equal price (within $0.01) → underline (color red)
• different price → replace, new_text = the source price ($278.20); write it even if lower — it documents the discrepancy.
• source line with no target match → note (red/yellow) near the target's matching section header (row_top), text like H'Lamp bracket $105.30 .7b. Stack multiple by adding ~12 to top. Skip pure procedure lines (scans, transport, masking, alignment, cleanup) — they're on both under different wording.

4. COST pass — ESTIMATE TOTALS (labor hours & rates)

Compare each labor category (totals[]: Body Labor, Paint Labor, Mechanical Labor, Paint Supplies) between source and target. For any differing hours or rate, highlight the target's hrs_bbox / rate_bbox and set new_text to the source value. This surfaces rate disputes (e.g. $60/hr vs $75/hr) that don't show at the line level. If a category exists on one estimate only, note it rather than highlight.

5. OEM COMPLIANCE pass

Read references/oem_position_statements.md. Scan the target estimate's line items and its bottom-of-document legal/disclosure text for the triggers listed there: aftermarket/used markers (A/M, LKQ, RECOND, NAGS, OPT/ALT OEM, CAPA), ADAS work missing pre/post scans or calibration, reused one-time-use parts, and warranty/availability/like-kind-quality language. For each, add a note in the OEM color ("color":"blue","hl":"cyan") citing the position. To avoid covering line text, place OEM notes in the page bottom margin (top ~= page height - 85, stacking +13 each) and key each to its line number, e.g. OEM (Ln 23): MB requires new OEM part (MBUSA parts stmt); optionally add a blue underline on that line's price to tie it.

Sourcing order: (1) search the user's Google Drive OE-docs library (search_files, e.g. fullText contains 'Mercedes position statement bumper') and attach/link the matching statement; (2) if not found, run Serper (scripts/oem_search.py "<make> <topic> position statement", needs SERPER_API_KEY) — or the built-in web search if the key is unset — and cite the source URL; (3) fall back to the bundled reference summary. Never invent a citation — if unsupported, flag OEM: verify position statement instead.

Also consider jurisdictional law (see the reference file's jurisdictional section) using the owner's/shop's state from the estimate header — e.g. aftermarket-parts disclosure/consent or OEM-procedure requirements. Law changes, so verify the current statute (Serper / web search, or a legal doc in Google Drive) before quoting it in a dispute. This OEM pass is the input to an OEM citation density report: aim to attach the strongest citation (OEM procedure/position statement, then ADAS requirement, then applicable law) to as many flagged items as possible.

6. Build instructions & render

Combine all annotations into one JSON {"annotations":[...]} and run:

python scripts/annotate_pdf.py "<target>.pdf" /tmp/instructions.json "<target> annotated.pdf"

7. Verify before delivering

Render a few pages and read them back:

pdftoppm -png -r 110 -f 3 -l 6 "<target> annotated.pdf" /tmp/check

Confirm strikes land on the right numbers, stamped values are legible and clear the hrs//hr units, notes don't cover other text, and OEM flags read blue (distinct from red cost gaps). Nudge top/x and re-run if anything overlaps. Then summarize for the user: count of under-valued lines, labor/rate gaps, missing items, and OEM flags — and hand over the PDF.

Edge cases

• More than two estimates: compare pairwise; ask which pairing if ambiguous.
• Scanned/flattened PDFs: extractor needs real text; if it returns no priced lines, tell the user to supply a text-based estimate (or OCR first).
• Don't invent values or citations. Every stamped number comes from extracted data; every OEM cite comes from the reference file, Google Drive, or Serper/web. When unsure, leave it unmarked and mention it.