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Structured Extraction

OrganizationPopular
bionic-gpt
structured-extraction

Extract structured, source-located evidence from uploaded documents with Xberg for document analysis and validation.

Overview

Publisherbionic-gpt
Repositorybionic-gpt
Skill namestructured-extraction
Stars
2.4K
Forks
247
Bundled files
Instructions only
LicenseApache-2.0
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 bionic-gpt on GitHub. Read the source before you install it.

Installation

Install the Structured 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/bionic-gpt/bionic-gpt/tree/main/crates/builtin-skills/skills/structured-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/bionic-gpt/bionic-gpt.git /tmp/bionic-gpt
mkdir -p .claude/skills
cp -r /tmp/bionic-gpt/crates/builtin-skills/skills/structured-extraction .claude/skills/structured-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Structured 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 Structured 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 Structured 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.

Structured Extraction

Use this skill when a task depends on facts contained in uploaded documents or other files and those facts must be extracted before analysis.

Workflow

  1. Read the ## Current attachments section in the system prompt. It gives each original filename plus its preprocessed Markdown and original-file paths.
  2. For each source, call document_conversion_api_extractdocument directly with run_python only when the preprocessed Markdown is missing or a fresh extraction is explicitly needed. Pass the original path as file_path.
  3. In that Python call, serialize the complete result to /home/user/work/<source-name>.json instead of printing it. For example:
python
import json

result = document_conversion_api_extractdocument(
    file_path="/home/user/attachments/<attachment-id>/original/<source-file>"
)
with open("/home/user/work/<source-file>.json", "w") as extracted:
    extracted.write(json.dumps(result, ensure_ascii=False, indent=2))
print("Saved extraction to /home/user/work/<source-file>.json")
  1. Prefer reading or searching the preprocessed /home/user/attachments/<attachment-id>/content.md with read_file or run_bash commands such as grep; it remains available across later turns. Work files also persist without appearing as generated files in the conversation.
  2. Preserve the source filename and every available location marker when reporting evidence. Keep extraction separate from interpretation.
  3. If conversion fails or a format is unsupported, report the exact failure and do not substitute guessed content.

Xberg supports many document families, including PDF, Word and other office documents, spreadsheets, presentations, ebooks, email, archives, HTML/XML, structured text, images with OCR, and audio or video transcription. Let the conversion response determine whether a particular file is supported.

Quality checks

  • Every extracted claim has a source filename and location when available.
  • Tables retain their headers and row or column meaning.
  • Empty, unreadable, or missing content is marked as unavailable.
  • Extraction and interpretation remain separate.

Frequently asked questions

What does the Structured Extraction AI skill do?

Extract structured, source-located evidence from uploaded documents with Xberg for document analysis and validation.

Why use Structured Extraction on TypingMind?

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

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

Which AI models can use Structured 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 Structured Extraction?

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

Is the Structured Extraction AI skill free?

Yes. It is published on GitHub by bionic-gpt under the Apache-2.0 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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