# Land Plan Estimator > Read, index, measure, and estimate from land-development / civil site plan sets delivered as PDFs (subdivision layouts, grading & drainage, utility plans, road plan-and-profile, erosion control, landscape/buffer plans, construction details, boundary/survey). Use this ANY time the user uploads or points to a plan set, drawing set, "the civils", a sheet index, or a large multi-sheet PDF and wants to query it, summarize sheets, pull quantities (lot counts, disturbed acreage, cut/fill, pipe sizes, pavement areas, curb LF, buffer LF, setbacks), prepare a land-development budget takeoff, or find which sheet a given detail lives on. Trigger even if the user just says "read these plans" or "what does sheet C-3 say" — do NOT try to stuff the raw PDF into context, use this preprocessing-and-index workflow instead. - Skill: `dhinc1/land-plan-estimator` (Agent Skill, multi-file: 11 files) - Install (CLI): `npx skillmds@latest add dhinc1/land-plan-estimator` - Raw SKILL.md: https://api.skillmd.com/api/skills/dhinc1/land-plan-estimator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: Dhinc1 (https://skillmd.com/u/dhinc1) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/dhinc1/land-plan-estimator --- # Land Plan Estimator ## Why this exists Civil plan sets are large-format, multi-sheet PDFs (often 15-40MB, ARCH/ANSI D sheets). Dumping the whole set into context is slow, expensive, and inaccurate — the more you jam in, the worse the answer. The fix is a one-time preprocessing step that turns each sheet into cheap, structured data: split the set, pull the vector text layer WITH coordinates, detect scale, rasterize each sheet, then write a compact per-sheet summary plus a routing index. After that, answering a question means reading a small index and one or two sheet summaries. A takeoff means MEASURING off that structured data with a script — never eyeballing a rendered image and typing a number. Everything is file-backed and lives next to the source PDF. No external database required (an optional SQLite layer exists for a complex set with many schedule-driven structures — see Step 1b). **v2 note:** this version folds in the parts of ContractorOS's construction-takeoff and drawings-analyser skills (reviewed and adapted 2026) that would have caught this business's real estimating mistakes: a storm pipe size transposed between two adjacent runs, a duplicate paving lift double-charged, and a curb quantity pulled from a `2 × centerline` formula proxy instead of a measurement. See "Field-tested gotchas" below — each one cost a real takeoff on this project. ## The workflow ### 1. Preprocess (`split_extract.py`) First confirm the set is CAD vector (Civil3D/AutoCAD exports carry a clean text layer). Run the preflight, then the full pass: ```bash pip install --break-system-packages pymupdf pdfplumber # if not present python scripts/split_extract.py "PATH/TO/plans.pdf" --check-only # vector vs scanned python scripts/split_extract.py "PATH/TO/plans.pdf" --split-pdf # full run ``` `--check-only` reports how many sheets have a usable text layer and stops. `--split-pdf` is REQUIRED, not optional, in v2 — it writes the single-sheet PDF every downstream `measure.py` call and the final Bluebeam-safe markup both need. Add `--sqlite` for a local `plans.db` manifest index when you want cross-project SQL later; for a single set the `.md` index is enough. Accepts a merged set or a directory of PDFs. Writes a `*_sheets/` folder with, per sheet (numbered sequentially across the whole set — `sheet_001`, `sheet_002`, ...): `sheet_NNN.png` (raster), `sheet_NNN.txt` (plain text dump, for cheap reading), `sheet_NNN.pdf` (single sheet, for measuring/markup), `sheet_NNN.json` (word-level text WITH coordinates, title-zone flags, detected scale, vector geometry counts), and a `manifest.json` indexing all of it. Read `manifest.json` first. Key fields per sheet: `titleblock_hint`, `has_text_layer`, `text_chars`, `scale` (detected factor + confidence — see below), `vector_drawing_count`. If `scanned_sheets_no_text_layer` is non-empty, those sheets are raster only. If `sheets_no_scale_detected` is non-empty, read that sheet's own scale note off the render before measuring anything on it — never assume a scale. Tune `--long-edge` if title-block or dimension text is unreadable in the raster: raise to ~2800 for dense sheets, lower to ~1600 to save tokens on simple ones. **Scale detection is metric AND imperial**, because this business's plan sets are imperial (`SCALE:1" = 40'`, `SCALE:1" = 60'`, `1/8"=1'-0"` for details) — not `1:100`. It rejects a bare `1:N` sitting next to FALL/SLOPE/GRADE/BANK/BATTER (civil sheets are full of "MIN 1:50 FALL" slope callouts, not scales). Still confirm a detected scale against a printed dimension or a known feature (a lot width, a ROW) before trusting any length/area off it — see the scale-check gotcha below. ### 1b. Optional deep index (structured DB) — for a schedule-heavy set For a set with many schedule-driven structures (storm structures, sanitary manholes, a long plant schedule) where you'll query "how many / where / does the plan count match the schedule" more than once, build the structured layer instead of re-reading text each time: 1. **Extract instances per plan sheet** (never on a schedule sheet — a tag in a schedule is a DEFINITION, not a placed instance): ```bash python scripts/extract_instances.py sheet_015.json --pattern "1A-\d+" \ --sheet sheet_015 --exclude "1500,980,1850,1180" -o takeoff/instances_1A.json ``` `--exclude` drops the schedule/legend box read off one overview render. `--space-tolerant` handles CAD letter-spaced tags ("1 A - 1 1"). 2. **Assemble `structured.json`** — Claude's judgment, not a script: a `schedules` table (type → size/material/spec, from the schedule), an `instances` table (from step 1, one row per placed tag with x/y/sheet), and `notes` (general-notes callouts). Every row carries a `reliability` (HIGH text / MEDIUM vision / LOW scaled). 3. **Build the queryable database:** ```bash python scripts/build_db.py takeoff/structured.json -o takeoff/plans.sqlite ``` 4. **Validate provenance — mandatory if you built the DB.** This is the single highest-value check in v2: it confirms every value's distinctive tokens actually appear on its cited source sheet, and flags or relocates the row when they don't. ```bash python scripts/validate_provenance.py takeoff/structured.json \ --textdir "PATH/TO/plans_sheets" --fields fields.json \ --apply -o takeoff/provenance_report.json ``` `fields.json` maps `{table: [id_field, value_field, source_field]}`. Report every relocation and flag to the user — don't silently apply and move on. Skip Step 1b entirely for a small or simple set — reading the per-sheet `.txt`/`.json` directly is faster when there's little to reconcile. ### 2. Summarize each sheet For every sheet, read its `.txt` (or `.json` for coordinates), and Read its `.png` (the image carries the linework, callouts, and layout the text layer misses). Then write `sheet_NNN.md` using the template below. Skip the image only when the text is already complete for a text-only sheet (general notes). Work through sheets in order. For a large set, batch it and note progress. ### 3. Build the routing index Write `_index.md` in the sheets folder: one line per sheet mapping sheet number → title → discipline → what lives on it → key quantities → detected scale. This is what you read first on every future query to decide which sheet(s) to open. ### 4. Answer queries Read `_index.md`, route to the relevant sheet(s), read those `.md` files. For counts/locations/relationships when a structured DB exists (Step 1b), query it — exact and cheap. Open the `.png` (or the single-sheet `.pdf`) only when the summary is insufficient or the question is inherently visual. For any geometry question (an area, a length, a dimension not already captured), use `measure.py` at query time — never eyeball a scaled distance off the render and state it as a number. ## Estimating from a plan set When the task is a takeoff or estimate, not just Q&A: 1. Read `references/scope_kickoff.md` and have the user fill it (or state scopes inline). Only estimate the divisions they include. 2. Use `references/pay_items.md` as the target list and section order. Measure for those items in the included divisions. Add items the plan shows that the catalog lacks; never silently drop scope. 3. Break out segregated scopes into their own sections: pump/lift station, package plant/WWTP, and each offsite roadway. Catch offsite by callout (a named public road, "OFFSITE", NCDOT encroachment, a station range beyond the boundary), not by where it sits in a sheet, and record the station limits so onsite and offsite never double-count. 4. Carry the same exclusions the bids use (geotech, SWPPP inspection, import/export, rock, permits) so an estimate lines up against a real bid. 5. **Transcribe schedule tables in full — never eyeball-summarize one.** Pipe schedules, structure schedules, and plant schedules (storm/sewer FROM-TO-SIZE-LENGTH tables, curb inlet/drop inlet/FES counts, buffer plant lists) are the sheet's ground truth. Use `measure.py tables` (pdfplumber) against the schedule's bbox, or pull every row into a small script, sum by category programmatically, and only then compare to whatever number you had before. A schedule with 40+ rows is exactly where a visual skim mis-sizes one segment (15" read as 12", or the reverse) — that class of error doesn't show up as a wrong total, it shows up as two adjacent line items each wrong by the same amount in opposite directions. If two pipe (or structure) sizes in the same network look off by a matching amount, that's the signature — go re-pull the table. **This happened on Cottages at Back Creek**: a 56' run was read as 15" when the schedule said 12", overstating one line and understating the other by the same 56'. 6. **Variable-width or tapered geometry needs a station-by-station takeoff, not a plan-view label.** Turn lanes, roadway widenings, tapers, and anything else that isn't a constant cross-section over its length cannot be measured from a single width callout. Pull the actual cross-section sheets (usually keyed by station), record the width at each station, and compute area with average-end-area (length × average of the two end widths) per segment. Treat a uniform-width assumption for this kind of item as a placeholder to be replaced, not an estimate to defend. 7. **Before dropping new numbers into an existing budget template, audit the whole section you're touching — not just the rows you plan to edit.** Dump every row in that category (labels, quantities, formulas) and check for (a) another row with the same or a near-duplicate label that's already active, (b) formulas that still reference an input cell and fire even though the row looks like an unused template default. **This happened on Cottages at Back Creek**: two near-identical "1.5" intermediate course" paving rows were simultaneously active, effectively double-charging one lift — caught only because Dave asked for a reconciliation against the plan's own pavement section. Re-run this audit any time you add a line to a category. ### Measuring primaries with `measure.py` Do the actual measuring off the durable single-sheet PDFs, one sheet at a time. `measure.py` writes JSON to stdout (redirect or capture it). See its own docstring (`python scripts/measure.py --help`) for the full command reference; the essentials: - **Counting (tagged items):** `measure.py count --tags ... --exclude-bbox ...` — the reliable way to count a tagged structure/fixture. Pass the exact tag strings you learned from the legend/schedule. **Three-way cross-check, not optional:** the text-layer count, the schedule's stated Qty, and a visual pass over the markup (Step 6) must agree. If they diverge and you can't reconcile them, say so and drop the confidence. - **Lengths (pipe runs, curb, buffers):** `measure.py length --bbox ... --scale ...` returns every polyline in the region with length in FEET, stroke colour, and width, plus a `by_stroke` rollup. Isolate the item by stroke (matched to the legend) and region, then sum. **Prefer a plan-stated length when one exists** (`measure.py dimensions` over the label) — a called-out "125.00'" beats a computed polyline. - **Areas (basins, pavement zones, buffers, clearing limits):** `measure.py polygons --bbox ... --scale ...` returns area in SF and acres, largest-first. Validate against the visual before trusting it. - **Depths / volumes:** volume is always area × depth — never measured directly. Read the depth from a section, detail, or schedule (`measure.py dimensions` or `tables`), then multiply. If it genuinely isn't on the drawings, use a stated default and write the assumption into the takeoff. - **Schedules:** `measure.py tables --bbox ...` (pdfplumber) reads a cropped schedule region as structured rows. Don't rely on auto-detection to LOCATE the schedule on a busy sheet — crop to its region first. Pass `--scale` as an engineering ratio (`--scale "1:480"`), a plan-stated imperial note (`--scale "1\"=40'"`), or an explicit `--ft-per-pt` override. **Always confirm the detected/stated scale against a grid-spacing regularity check or a printed dimension before trusting any length/area from it** — a wrong scale throws areas off by the square of the error and volumes by the cube. ### Write the element ledger as you measure — `takeoff/elements.json` The assemblies (primary measurement → derived pay items) are the takeoff's *derivation* layer. The ledger is its *evidence* layer: one machine-checkable record of everything found, defined, and measured, so the reconciliation pass (below) runs mechanically instead of by recollection. It costs almost nothing — `count`, `polygons`, and `length` already return every coordinate; the discipline is writing them down in one place as you go. Record three kinds of entry: - **Definitions** — every tag a schedule defines: `{tag, sheet, schedule_qty (if the column is filled), stated_size_or_length (if any)}`. - **Instances** — every counted hit: `{tag, sheet, x, y, source: "plan" | "schedule" | "detail"}`. Only `source: "plan"` instances are additive. - **Measurements** — every Linear/Area/Volume primary: `{item, kind, value, units (LF/SF/AC/CY), sheet, scale, method, confidence, notes}`. ### Reconcile before it ships (do not skip) A takeoff that hasn't been checked against itself and the plan is a draft, not a deliverable. Two passes: **Deterministic, over `takeoff/elements.json`:** 1. **Schedule vs. plan** — for every tag with a stated schedule Qty, the plan-instance count must equal it. Divergence gets reconciled (wrong exclude box, a phantom detail-view duplicate) or the line's confidence drops with the discrepancy stated in the notes. This is the check that catches a mis-sized pipe run or a miscounted structure before it ships. 2. **Cross-sheet duplicates** — the same structure/fixture shown on a plan AND a detail/profile view is ONE item; a detail-view instance is never additive. 3. **Section audit** — before a new line goes into the budget template, the whole category it joins gets dumped and checked for duplicate-label rows or stale formulas still referencing an input cell (item 7 above). **Independent — a blind subagent anchor-dimension check, for every length/area that isn't a plan-stated number:** the person who measured something will "see" agreement with their own number even when it's wrong — the same reason a single radiologist re-reading their own scan catches less than a second reader would. Use the Agent tool to spawn a subagent that has **not** seen your total: give it the sheet(s), the scale, and the measurement's geometry/region — never your computed answer or your narrative — and have it: 1. Derive an **independent anchor** from a different source than the measurement being checked (a stated ROW width, a lot dimension, the distance between two grid/station callouts, the site boundary). 2. Check the measurement's plausibility against that anchor and return a ratio and a verdict: `plausible` (in a sensible band) or `out-of-band` with a suspected cause (wrong scale, wrong region, a formula-proxy standing in for a measurement, a double-counted segment). **This is exactly the check that would have caught the curb-quantity problem on Cottages at Back Creek**: the only active curb line used `= 2 × road centerline` — a convenience formula that systematically misses cul-de-sac bulb curb and intersection returns — and it looked plausible enough on its own that it shipped unchecked until Dave supplied his own measured total. An anchor check against the actual curb linetype (or, if none exists, an explicit `verify` flag on the formula-proxy number) is what should have run first. Treat any `out-of-band` verdict as a correction trigger: re-measure, and only ship the figure if it survives re-measurement, with the anomaly explained in the notes. ## Mark up the drawings (Step 6) A takeoff shouldn't live only in a spreadsheet. Mark every sheet you measured — one translucent box per counted item (`count --markup-out` builds the spec automatically, one colour per tag), or a shaded polygon for a measured area/extent. **A guessed "zone" box standing in for a real count is banned** — mark per item, driven by the item's own captured coordinates, or ship no overlay for that line. **Bluebeam-safe output is required for the final deliverable markup.** `measure.py markup` (PyMuPDF annotations) is fine for a quick on-screen check, but a PyMuPDF-saved PDF will **not open in Bluebeam Revu** — it rewrites the document structure in a way Bluebeam's parser rejects (Chrome/ Adobe tolerate it, which hides the problem until the file reaches the person who actually uses Bluebeam). Overlay onto the ORIGINAL single-sheet PDF with `pikepdf` instead, and save without object streams: ```python import pikepdf, json from pikepdf import Name, Dictionary, Array def hexrgb(h): h = h.lstrip('#') return tuple(int(h[i:i+2], 16) / 255 for i in (0, 2, 4)) pdf = pikepdf.open("sheet_015.pdf") # the ORIGINAL single-sheet PDF, not a PyMuPDF re-save pg = pdf.pages[0] res = pg.obj["/Resources"] H = float(pg.obj["/MediaBox"][3]) - float(pg.obj["/MediaBox"][1]) # read per page, never hardcode egs = res.get("/ExtGState") or pdf.make_indirect(Dictionary()); res["/ExtGState"] = egs ops = json.load(open("markup_015.json"))["ops"] body = ["q"] for i, op in enumerate(ops): x0, y0, x1, y1 = op["rect"] r, g, b = hexrgb(op.get("fill", "#1E88E5")) gname = f"/GSmk{i}" egs[Name(gname)] = Dictionary({"/ca": op.get("opacity", 0.35), "/CA": 1.0, "/BM": Name("/Normal")}) # PyMuPDF rect (y-down, top-left) -> PDF content stream (y-up, bottom-left): flip Y by page height body += [f"{gname} gs", f"{r} {g} {b} rg", f"{x0:.2f} {H-y1:.2f} {x1-x0:.2f} {y1-y0:.2f} re f"] body.append("Q") st = pdf.make_stream(("\n".join(body) + "\n").encode()) c = pg.obj.get("/Contents") pg.obj["/Contents"] = Array([c, st]) if not isinstance(c, Array) else (c.append(st) or c) pdf.save("sheet_015_marked.pdf", object_stream_mode=pikepdf.ObjectStreamMode.disable) ``` For a polygon (area) overlay, emit a filled path (`x0 H-y0 m`, then `x H-y l` per vertex, then `f`) instead of a rectangle. **Vision cross-check, then correct — don't just sign off.** Render each marked sheet (`measure.py render`) and read it against the original. A definition still highlighted means the exclude box needs widening; marks floating off-target mean the scale or coordinates are wrong. **For every area/extent overlay, corner-check it**: verify each corner of the shaded polygon lands on the feature it bounds by reading the rendered page corner-by-corner. The area *number* can be right while the *shape* is on the wrong part of the sheet — a page `Rotate` or content-stream transform can do that, and only a corner check catches it. Never sign off on a "looks about right" glance. ## Output: the takeoff worksheet + the budget template The deliverable is two linked things, not one: 1. **A Takeoff Worksheet** (its own small workbook, or a tab appended to the budget workbook) — every primary measurement with its method (schedule / plan_count / polygon_area / polyline_length / annotated_dimension / estimate), confidence, the anchor-check ratio and verdict where one was run, the sheet it came from, and the rate SOURCE (the template's own default, or a verified bid-rate database — never leave this unstated). This is the audit trail: it lets the number in the budget template be checked without re-doing the takeoff. 2. **The line-item entries in `references/pay_items.md`'s target template** (the existing LD Budget Template workbook) — the actual deliverable Dave uses. Cross-reference each entry back to its Takeoff Worksheet row. Deliver both, plus the marked-up drawing PDF(s) from Step 6. ### Confidence: label it, don't fake it Three reliability levels, marked per line: - **High:** a quantity printed on the sheet or in a schedule (a stated pipe length, a schedule Qty, seeding acreage) — no measurement involved. - **Medium:** a clean polygon/polyline measurement at a CONFIRMED scale, cross-checked (three-way for counts, anchor-checked for lengths/areas) and in-band. - **Low, human-verify:** anything geometric that isn't independently checked, a scaled value at an unconfirmed scale, a default depth/rate assumption, or an out-of-band anchor check that wasn't re-measured to resolution. Flag these `verify`; never present a measured or assumed quantity as if it were plan-stated. A **formula proxy inside the budget template is not a High-confidence number**, even when it looks precise. `curb LF = 2 × road centerline` is a convenience default — it systematically misses cul-de-sac bulb curb and intersection curb returns, which is real length. Treat any such formula-driven quantity as `verify`: prefer a plan-stated or client- confirmed total, or a real measured curb linetype, when one is available — and say plainly when a number is a formula proxy standing in for a real measurement, not just when it's a raw eyeball takeoff. Anchor unit prices to the catalog's real bid numbers, but keep measured quantities separate from stated ones in the output so the human checks the right rows. **State which rate source you actually used** — the template's own column defaults, or a verified bid-rate database — rather than letting the distinction go unstated; if you don't know which it is, say so and ask before presenting a cost as sourced. Use this exact structure for per-sheet summaries so they're scannable and machine-routable. Omit fields that genuinely don't apply; never invent values — if something isn't legible, write `unclear` and note that the image should be checked. ```markdown # Sheet {number} — {title} - **Discipline:** {cover | survey/existing | site/lotting | grading & drainage | utilities (water/sewer) | storm/BMP | erosion control (E&SC) | road plan-profile | details | landscape | other} - **Scale:** {e.g. 1"=40' (factor 480, high confidence); or "not detected — verify"} - **Sheet no. in title block:** {C-3.0, etc.} ## What's on this sheet {2-4 sentences, plain language: what a reviewer would find here.} ## Key data - {lot count / lot range / typical lot dims — for lotting sheets} - {disturbed area, total site area — acres} - {cut / fill volumes — CY, if shown} - {pipe/utility sizes & materials: water main, sewer, storm} - {road: name, ROW width, pavement section, station range} - {setbacks / buffers / easements} - {BMP type & sizing, if a stormwater sheet} ## Notes & specs {general notes, construction notes, spec callouts, permit conditions on this sheet} ## Cross-references {"See C-5.0 for profile", detail bubbles pointing elsewhere, matchlines} ## Check-the-image flags {anything the text layer couldn't capture that a human should eyeball} ``` ## Land-development specifics to watch for - **Title blocks** sit bottom-right; `titleblock_hint` in the manifest usually captures the sheet number and title. Title-block text is sometimes rotated 90° on large-format sheets — if the hint comes back empty or garbled, read the sheet ID off the render instead. - **Quantities that matter for this business:** lot count and lot mix, disturbed acreage (permitting), cut/fill balance, water/sewer sizing, road lengths and pavement sections, BMP sizing, buffer/landscape LF and plant counts, offsite roadway/turn-lane scope. - **Boundary/survey sheets** carry bearings, distances, and the legal description — extract these verbatim from the text layer when present; they're precision-critical, so flag the image for a check rather than paraphrasing numbers you can't read. `measure.py dimensions` does NOT parse bearings/curve tables — read those off the render. - **Plan-and-profile sheets** pair a plan view (top) with a profile (bottom) keyed by station; note the station range and which utility/road the profile is for. - **Matchlines** mean a feature continues on an adjacent sheet — record both sides in Cross-references so routing works. - **Scanned/as-built sheets** (no text layer) are common in older or recorded plats — rely on the image and OCR only if you need bulk text. ## Field-tested gotchas (read these — each one cost a real takeoff) - **Tags are usually hyphenated** (storm structure "1A-11", "SD-2"). The counter keeps hyphens inside a matched tag — pass them exactly as written. If `count` returns 0 for every tag on a page that clearly has text, it prints a WARNING — never report zeros as "none found" without investigating (see the outlined-text gotcha below). - **A vector PDF can still have NO usable text layer — "outlined" CAD exports.** Some DWG/Civil3D-to-PDF exports flatten text to vector outlines, so `split_extract` reports a high `vector_drawing_count` with `has_text_layer` true (or near-zero text_chars) — this is NOT raster, yet `count`/`text` matches nothing. Confirm the signature with `measure.py text` over a region you can plainly see has labels: zero hits confirms outlined text. Switch to vision counting or the schedules/tables instead — don't keep retrying tags. - **Rotated sheets:** `count` reports hit x/y in the page's native (un-rotated) space; `--exclude-bbox` is read in render/display space — they don't line up on a rotated sheet unless you use `--exclude-bbox-native` (built from coordinates `count` just printed) or read the exclude box off a `render --grid` image. - **Scale is often not a clean callout.** Title blocks may say "AS INDICATED." Always run the grid-spacing/known-dimension check (a lot width, a ROW, an even station interval) before trusting any area/length — a wrong scale throws areas off by the square and volumes by the cube. - **Exclude the FULL schedule region, not one row.** A schedule can span many rows plus a typical-detail callout elsewhere on the sheet that repeats a tag — a tight exclude box misses the outlier and it counts as a phantom instance. - **The same structure/fixture on the plan AND a detail/profile view — count it once, off the plan.** Treat any non-plan appearance as a cross-check, never additive. - **A scheduled tag may be a LINEAR item, not a count.** Check the schedule's SIZE/LENGTH column — a headwall or box culvert callout with a stated run length means the tag labels a length, not an EA. - **Diagrammatic/NTS routing (utility crossings, some erosion-control schematics):** you can still trace and mark the route, but never attach a scaled length — report a derived allowance and flag it Low. Read the general notes for "diagrammatic"/"schematic"/"NTS" before trusting any traced-pipe length. - **Never ship a guessed "zone" rectangle as count markup.** Markup is per item, driven by the item's own captured location, or none at all. ## Reference files - `references/scope_kickoff.md` — read at the start of any estimate. What divisions to measure, segregated scopes, exclusions. - `references/pay_items.md` — the target pay-item catalog (civil divisions, imperial units, takeoff basis per item) built from real bids. Enrich it as new projects add items; it also carries specific gotcha notes (pipe class, paving lift counts, curb formula-proxy caveat). ## Scripts - `scripts/split_extract.py` — split + raster + vector-extract (text with coordinates, scale detection, geometry counts) per sheet. Run once per plan set. - `scripts/measure.py` — the measurement engine: count / polygons / length / dimensions / tables / text / page-info / render / markup. Run at query time, one sheet at a time. - `scripts/extract_instances.py` — tag → coordinate-grounded instance, for the optional structured-DB path (Step 1b). Schedule-region exclude built in. - `scripts/build_db.py` — load `structured.json` into a queryable SQLite db. - `scripts/validate_provenance.py` — confirm every DB value's tokens actually appear on its cited sheet; relocate or flag when they don't. Run this any time Step 1b is used — it is the single highest-value accuracy check in this skill. ## When a summary isn't enough If a query needs exact geometry, a dimension you can't read at the current raster, or a detail callout, open `sheet_NNN.png`; if still ambiguous, open `sheet_NNN.pdf` (vector, zoomable) or re-rasterize that one sheet at a higher `--long-edge`. Reserve full-PDF reads for genuine whole-set questions.