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Batch Extraction

OrganizationPopular
xberg-io
batch-extraction

Use when extracting from many files at once with shared config, bounded parallelism, per-file overrides, and error recovery. Covers the `batch` command, `--file-configs`, `--max-concurrent`, and output layout.

Overview

Publisherxberg-io
Repositoryxberg
Skill namebatch-extraction
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 Batch Extraction 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/batch-extraction
  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/batch-extraction .claude/skills/batch-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Batch Extraction 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 Batch Extraction 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 Batch Extraction 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.

Batch extraction

Use this when processing a directory or glob of documents in one pass. xberg batch shares one extraction config across every file, runs extractions concurrently, and returns one structured array — failures on individual files do not abort the run.

Basic usage

bash
# Glob expands to many paths; results come back as a JSON array (default)
xberg batch *.pdf

# Mixed formats, markdown content for LLM ingestion
xberg batch docs/*.docx --content-format markdown

# Recurse with the shell, then extract
xberg batch $(find ./corpus -name '*.pdf')

batch defaults to --format json (vs --format text for single extract). Each array entry is a full extraction result, so downstream code can index by position into the input path list.

bash
xberg batch reports/*.pdf \
  | jq '.[] | {chars: (.content | length), mime: .mime_type}'

Parallelism

--max-concurrent caps how many files extract at once. When omitted, the scheduler derives document concurrency from the total thread budget. Lower it on memory-constrained hosts or when OCR/ML models are active, since each in-flight extraction holds its own buffers. Layout-heavy batches are further limited (1 concurrent extraction for all-PDF-layout batches, 2 for mixed layout):

bash
# Cap at 4 concurrent extractions
xberg batch scans/*.pdf --ocr true --max-concurrent 4

--max-threads additionally caps total internal threads (Rayon, ONNX intra-op, the batch semaphore) for tightly constrained environments:

bash
xberg batch *.pdf --max-concurrent 2 --max-threads 4

Per-file config overrides

A single shared config does not always fit. --file-configs points at a JSON file mapping each path to its own override object, merged on top of the shared config for that file only:

json
{
  "scan.pdf": { "force_ocr": true },
  "report.pdf": { "output_format": "markdown" },
  "data.xlsx": { "output_format": "json" }
}
bash
xberg batch scan.pdf report.pdf data.xlsx --file-configs overrides.json

Keys are file paths (matching the paths passed on the command line); values are per-file extraction config objects in snake_case, the same shape as a config file.

Output layout

For text/toon output with image extraction, --output-dir controls where referenced image files (e.g. image_0.png) are written; the directory must already exist. JSON output embeds image bytes inline and ignores --output-dir.

bash
mkdir -p out/images
xberg batch slides/*.pptx --extract-images true --output-dir out/images --format text

Error recovery

Batch extraction is fault-tolerant per file: one unreadable or corrupt document does not stop the rest. Inspect results for partial content and surfaced errors rather than relying on the process exit code alone. Pair with --max-concurrent to avoid exhausting memory when a few large files sit in a big batch.

Shared config

Every extract flag also applies to batch (OCR, chunking, layout, content format, etc.) and is shared across all files unless a --file-configs entry overrides it:

bash
xberg batch invoices/*.pdf \
  --layout --layout-table-model slanet_wireless \
  --content-format markdown --max-concurrent 8

A config file works too and auto-discovers from the cwd upward:

toml
output_format = "markdown"

[ocr]
backend = "tesseract"
language = "eng"
bash
xberg batch corpus/*.pdf --config xberg.toml

Programmatic access

From Python, extract_batch takes a list of ExtractInputs and returns one envelope whose results array holds a document per input:

python
from xberg import ExtractInput, extract_batch, ExtractionConfig

config = ExtractionConfig(output_format="markdown")

inputs = [ExtractInput(uri=p) for p in ["a.pdf", "b.docx", "c.xlsx"]]
output = await extract_batch(inputs, config)

for doc in output.results:
    print(len(doc.content))

Per-input overrides go on ExtractInput.config (a FileExtractionConfig). Node.js mirrors this with extractBatch; Rust uses extract_batch(inputs, &config). See references/python-api.md, references/nodejs-api.md, and references/rust-api.md in the sibling xberg skill.

MCP

When the xberg MCP server is registered, prefer the extract_batch tool over shelling out — it takes an array of input objects and a config object and returns structured results directly.

Common pitfalls

  • Default format differs — batch defaults to --format json, extract to --format text. Set --format explicitly if a script depends on one shape.
  • --output-dir must exist — the CLI does not create it.
  • Memory blowups — large batches with OCR/layout active may need an explicit --max-concurrent ceiling.
  • --file-configs path keys — must match the paths as passed on the command line, not absolute-resolved variants.

See references/cli-reference.md for the full batch flag set.

Frequently asked questions

What does the Batch Extraction AI skill do?

Use when extracting from many files at once with shared config, bounded parallelism, per-file overrides, and error recovery. Covers the `batch` command, `--file-configs`, `--max-concurrent`, and output layout.

Why use Batch Extraction on TypingMind?

Because you install it once and use it with any model. Batch Extraction 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 Batch Extraction in TypingMind?

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

Which AI models can use Batch Extraction?

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 Batch Extraction?

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

Is the Batch Extraction 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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