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Get Started Free →Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.
.claude/skills/xberg-io-extracting-tables/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -15% | 0% |
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Use this when the user wants structured tabular data — financial statements, scientific tables, invoices, spreadsheet-style PDFs. Xberg detects tables via a layout model (RT-DETR v2) and reconstructs cell structure with a configurable table model.
bash# Markdown tables embedded in the content stream xberg extract report.pdf --layout --content-format markdown # Structured JSON output, tables appear under result.tables xberg extract report.pdf --layout --format json
--layout turns on layout-aware extraction; without it, tables fall back to plain text reflow and you lose cell boundaries.
Two surfaces, picked via --format (CLI shape) and --content-format (content rendering):
content — --content-format markdown. Tablesappear inline as | col | col | blocks. Good for LLM ingestion.
tables array — --format json. Each entry hascells[][] (rows × cols), markdown (pre-rendered), page_number, bounding_box. Use this when downstream code needs exact cell access. (bounding_box is omitted when no position data is available.)
Both are populated at once when --layout is on. The tables array is always structured; the content stream switches representation.
bashxberg extract financials.pdf --layout --format json \ | jq '.result.tables[] | {page: .page_number, rows: (.cells | length)}'
--layout-table-model picks the reconstruction backend:
| Model | Best for | Notes | | ------------------ | ----------------------------------------------------- | ------------------------------------------- | | tatr | dense complex tables (academic, financial) | Default. Heaviest, highest accuracy. | | slanet_auto | dispatches per-table to wired/wireless | Good when table styles are mixed. | | slanet_wired | tables with visible borders | Faster than tatr. | | slanet_wireless | tables without borders (whitespace-separated) | For invoices, simple grids. | | slanet_plus | hybrid wired / wireless | Lighter than slanet_auto. | | disabled | layout detection only, no table structure | Use to skip table model cost. |
bashxberg extract bank-statement.pdf \ --layout --layout-table-model tatr --content-format markdown
Drop --layout-confidence when the layout model misses tables (default threshold ~0.5):
bashxberg extract noisy-scan.pdf --layout --layout-confidence 0.3
.xlsx, .ods, .csv, .tsv are extracted by dedicated parsers — no layout model needed. Each sheet becomes a markdown table (or structured table) automatically:
bashxberg extract workbook.xlsx --content-format markdown xberg extract data.csv --format json
Pass --no-cache=true only when iterating on the same file with different configs.
toml# `output_format` in config files equals `--content-format` on the CLI. output_format = "markdown" [layout] confidence_threshold = 0.5 table_model = "tatr"
Then:
bashxberg extract report.pdf --format json
From Python, structured tables live on the document in the result envelope (result.results[0].tables):
pythonfrom xberg import ExtractInput, extract, ExtractionConfig, LayoutDetectionConfig config = ExtractionConfig( layout=LayoutDetectionConfig(table_model="tatr"), output_format="markdown", ) result = await extract(ExtractInput(uri="report.pdf"), config) for table in result.results[0].tables: print(table.markdown) # rendered markdown print(table.cells[0][0]) # cell access
Node.js mirrors this (extract, output.results[0].tables, camelCase fields). See references/python-api.md and references/nodejs-api.md in the sibling xberg skill for full type signatures.
region; the merge is not preserved as metadata.
--ocr-auto-rotate true for image-basedPDFs before extraction.
lost.
tables[] entry.Stitch by matching column headers if needed.
WASM builds and on the Android x86_64 emulator; native targets ship full support.
tables with --layout on — confidence threshold too high ortable model mismatched. Drop --layout-confidence to 0.3, try --layout-table-model tatr.
--layout-table-model toslanet_wired for bordered grids or slanet_wireless for invoices.
tatr is heavy. Use slanet_auto orslanet_plus as a default; reach for tatr only when accuracy matters.
See references/cli-reference.md for the full layout flag set and references/advanced-features.md for the layout pipeline internals.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,281 | 5,924 | -52% | 1 | 1 | 0% | 1,900 | 2,401 | +26% | 0 | 0 | — |
case-02 | fail→pass | 14,235 | 33,258 | +134% | 1 | 1 | 0% | 2,482 | 2,842 | +15% | 0 | 0 | — |
case-03 | fail→pass | 15,541 | 4,845 | -69% | 1 | 1 | 0% | 1,996 | 2,183 | +9% | 0 | 0 | — |
case-04 | fail→pass | 12,001 | 5,132 | -57% | 1 | 1 | 0% | 1,804 | 2,315 | +28% | 0 | 0 | — |
case-05 | fail→pass | 16,725 | 4,484 | -73% | 1 | 1 | 0% | 2,506 | 2,118 | -15% | 0 | 0 | — |
case-06 | fail→pass | 11,281 | 4,771 | -58% | 1 | 1 | 0% | 1,448 | 2,239 | +55% | 0 | 0 | — |
case-07 | fail→pass | 15,787 | 4,087 | -74% | 1 | 1 | 0% | 2,255 | 2,063 | -9% | 0 | 0 | — |
case-08 | fail→pass | 10,548 | 4,521 | -57% | 1 | 1 | 0% | 1,264 | 2,173 | +72% | 0 | 0 | — |
case-09 | pass→pass | 13,864 | 4,147 | -70% | 1 | 1 | 0% | 2,308 | 2,150 | -7% | 0 | 0 | — |
case-10 | fail→pass | 17,185 | 3,128 | -82% | 1 | 1 | 0% | 986 | 2,019 | +105% | 0 | 0 | — |
case-11 | fail→pass | 14,757 | 5,628 | -62% | 1 | 1 | 0% | 2,408 | 2,420 | +0% | 0 | 0 | — |
case-12 | pass→pass | 21,652 | 5,856 | -73% | 1 | 1 | 0% | 3,088 | 2,436 | -21% | 0 | 0 | — |
case-13 | fail→fail | 12,218 | 11,149 | -9% | 1 | 1 | 0% | 1,547 | 3,145 | +103% | 0 | 0 | — |
case-14 | fail→pass | 15,013 | 5,687 | -62% | 1 | 1 | 0% | 2,011 | 2,391 | +19% | 0 | 0 | — |
case-15 | fail→fail | 18,534 | 6,074 | -67% | 1 | 1 | 0% | 2,376 | 2,428 | +2% | 0 | 0 | — |
case-16 | fail→fail | 10,593 | 2,808 | -73% | 1 | 1 | 0% | 1,660 | 1,834 | +10% | 0 | 0 | — |
case-17 | fail→pass | 16,438 | 6,173 | -62% | 1 | 1 | 0% | 1,877 | 2,052 | +9% | 0 | 0 | — |
case-18 | pass→pass | 14,628 | 4,072 | -72% | 1 | 1 | 0% | 1,936 | 2,032 | +5% | 0 | 0 | — |
case-19 | fail→pass | 13,941 | 6,686 | -52% | 1 | 1 | 0% | 1,950 | 2,308 | +18% | 0 | 0 | — |
case-20 | fail→pass | 17,660 | 7,481 | -58% | 1 | 1 | 0% | 2,662 | 2,472 | -7% | 0 | 0 | — |
case-21 | pass→pass | 40,088 | 4,301 | -89% | 1 | 1 | 0% | 2,051 | 2,113 | +3% | 0 | 0 | — |
case-22 | fail→pass | 18,797 | 4,890 | -74% | 1 | 1 | 0% | 2,581 | 2,144 | -17% | 0 | 0 | — |
case-23 | pass→pass | 9,264 | 3,070 | -67% | 1 | 1 | 0% | 1,340 | 1,895 | +41% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +65 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 9/19/2026 | +68% |
| gemini-3.6-flash | verified | 9/9/2026 | +74% |
| gemini-3.6-flash | verified | 9/1/2026 | +86% |
| gemini-3.6-flash | verified | 8/17/2026 | +82% |
| gemini-3.6-flash | verified | 8/11/2026 | +73% |
Other measured skills in the registry, with their headline benchmark lift.