Xberg is a document-intelligence engine that reads files, URLs, archives, and source trees and extracts text, metadata, images, tables, and structured data, with additional code-language understanding. Developers use it through language bindings, a command-line tool, REST API, or MCP server, and the catalogue entries support those integrations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xberg-io/xberg --skill xberg-extracting-tablesgit clone --depth 1 https://github.com/xberg-io/xbergWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xberg-io/xberg/xberg-extracting-tables)<a href="https://agentmods.dev/skills/xberg-io/xberg/xberg-extracting-tables"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-extracting-tables/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xberg-io/xberg/xberg-extracting-tables"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-extracting-tables.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.01314 |
| Opus 5.5 | $0.00014 | $0.00526 |
| Sonnet 5.5 | $0.00007 | $0.00263 |
| Haiku 4.5 | $0.00004 | $0.00131 |
Grade A, and why
xberg-extracting-tables scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# 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
tablesarray —--format json. Each entry hascells[][](rows × cols),markdown(pre-rendered),page_number,bounding_box. Use this when downstream code needs exact cell access. (bounding_boxis 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.
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:
| 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. |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 155 lines · 36 tokens per session scan A de0b55520361
xberg-extracting-tables is a skill published in the GitHub repository xberg-io/xberg (9,418 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,314 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-15.
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