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Structured Extraction

Community
spyrae
structured-extraction

Turn pages that disagree about everything into rows that can be compared, without inventing the parts they left out.

Overview

Publisherspyrae
Repositorykronos-agent-os
Skill namestructured-extraction
Stars
66
Forks
14
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 spyrae 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/spyrae/kronos-agent-os/tree/main/templates/skill-packs/marketplace/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/spyrae/kronos-agent-os.git /tmp/kronos-agent-os
mkdir -p .claude/skills
cp -r /tmp/kronos-agent-os/templates/skill-packs/marketplace/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

Comparing anything means first making the sources agree on shape. Two listing sites will name the same thing three ways and omit a different field each. The whole job is doing that without quietly filling the gaps.

One schema, decided first

Write the fields down before fetching. For an offer of any kind, that is usually:

FieldNotes
namewhat the owner would call it
urlthe page this row came from — always
priceas shown
currencyexplicit on every row, never inferred from the site
one field per additional costdeposit, shipping, utilities, duty
sourcewhich site

Add whatever the specific question needs, and no more. A schema with fields nobody will compare is a schema that invites guessing.

The rule that matters

A field the page did not state is missing, not zero, and not "probably the usual". A listing without a stated deposit is not a listing with no deposit. Leave it out, or mark it absent, and let the comparison refuse to rank that row — compare_offers does exactly that, and the refusal is information.

Same for units and currency. 8.750.000 is a number written the way that site writes numbers; Rp 8.750.000 is a price. Carry the currency per row, because the moment two currencies meet in one total the total is wrong and looks right.

Method

  1. fetch_page on the item's own page — not the search listing.
  2. extract_structured with the field list. It runs a small model against the text and returns fields, which is cheaper and steadier than reading prose.
  3. Check the rows. If one field is empty on every row from one site, the pattern is wrong, not the site.
  4. For more than a handful of rows, or any arithmetic beyond adding, use run_code — a program that sums is checkable, a paragraph that sums is not.

Reporting

State per row where it came from and what was missing. A table that looks complete because the gaps were filled is worse than one with holes, because nobody can see which numbers to distrust.

Output

  • Rows in one shape, each with its source URL
  • What was missing, per row
  • Which sources could not be read at all

Frequently asked questions

What does the Structured Extraction AI skill do?

Turn pages that disagree about everything into rows that can be compared, without inventing the parts they left out.

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/spyrae/kronos-agent-os/tree/main/templates/skill-packs/marketplace/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 spyrae 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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