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Extracting Tables

OrganizationPopular
xberg-io
extracting-tables

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.

Overview

Publisherxberg-io
Repositoryxberg
Skill nameextracting-tables
Stars
9.4K
Forks
590
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xberg-io on GitHub. Read the source before you install it.

Installation

Install the Extracting Tables AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/xberg-io/xberg/tree/main/plugin/skills/extracting-tables
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xberg-io/xberg.git /tmp/xberg
mkdir -p .claude/skills
cp -r /tmp/xberg/plugin/skills/extracting-tables .claude/skills/extracting-tables
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Extracting Tables in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Extracting Tables on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Extracting Tables is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Extracting tables

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.

Basic usage

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.

Output shapes

Two surfaces, picked via --format (CLI shape) and --content-format (content rendering):

  • Markdown tables in content — --content-format markdown. Tables appear inline as | col | col | blocks. Good for LLM ingestion.
  • Structured tables array — --format json. Each entry has cells[][] (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.

bash
xberg extract financials.pdf --layout --format json \
  | jq '.result.tables[] | {page: .page_number, rows: (.cells | length)}'

Table models

--layout-table-model picks the reconstruction backend:

ModelBest forNotes
tatrdense complex tables (academic, financial)Default. Heaviest, highest accuracy.
slanet_autodispatches per-table to wired/wirelessGood when table styles are mixed.
slanet_wiredtables with visible bordersFaster than tatr.
slanet_wirelesstables without borders (whitespace-separated)For invoices, simple grids.
slanet_plushybrid wired / wirelessLighter than slanet_auto.
disabledlayout detection only, no table structureUse to skip table model cost.
bash
xberg 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):

bash
xberg extract noisy-scan.pdf --layout --layout-confidence 0.3

Spreadsheets

.xlsx, .ods, .csv, .tsv are extracted by dedicated parsers — no layout model needed. Each sheet becomes a markdown table (or structured table) automatically:

bash
xberg 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.

Config file alternative

toml
# `output_format` in config files equals `--content-format` on the CLI.
output_format = "markdown"

[layout]
confidence_threshold = 0.5
table_model = "tatr"

Then:

bash
xberg extract report.pdf --format json

Programmatic access

From Python, structured tables live on the document in the result envelope (result.results[0].tables):

python
from 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.

Known limitations

  • Merged cells — reconstructed as repeated values across the spanned region; the merge is not preserved as metadata.
  • Rotated tables — enable --ocr-auto-rotate true for image-based PDFs before extraction.
  • Nested tables — flattened. Detection succeeds; structural nesting is lost.
  • Multi-page tables — each page yields a separate tables[] entry. Stitch by matching column headers if needed.
  • ONNX Runtime required — layout and table models are unavailable in WASM builds and on the Android x86_64 emulator; native targets ship full support.

Common failure modes

  • Empty tables with --layout on — confidence threshold too high or table model mismatched. Drop --layout-confidence to 0.3, try --layout-table-model tatr.
  • Markdown tables look ragged — switch --layout-table-model to slanet_wired for bordered grids or slanet_wireless for invoices.
  • Slow extraction — tatr is heavy. Use slanet_auto or slanet_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.

Frequently asked questions

What does the Extracting Tables AI skill do?

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.

Why use Extracting Tables on TypingMind?

Because you install it once and use it with any model. Extracting Tables is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Extracting Tables in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xberg-io/xberg/tree/main/plugin/skills/extracting-tables. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Extracting Tables?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Extracting Tables?

As many as you like. As long as a model supports skills, you can use Extracting Tables with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Extracting Tables AI skill free?

Yes. It is published on GitHub by xberg-io under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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