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Picking A Format

OrganizationPopular
xberg-io
picking-a-format

Use when choosing an output format for extracted documents — plain text, markdown, djot, HTML, JSON, or DocTags. Maps consumer (LLM, parser, archive) to the right `--format` / `--content-format` pair.

Overview

Publisherxberg-io
Repositoryxberg
Skill namepicking-a-format
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 Picking A Format 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/picking-a-format
  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/picking-a-format .claude/skills/picking-a-format
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Picking A Format 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 Picking A Format 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 Picking A Format 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.

Picking a format

Xberg has two orthogonal format knobs. Get them right up front and the downstream code stays simple.

KnobWhat it controlsValuesDefault
--formatHow the CLI prints the resulttext, json, toontext (extract), json (batch)
--content-formatHow extracted content is rendered inside resultplain, markdown, djot, html, json, doctagsplain
--token-reductionStrip whitespace / boilerplate for LLM contextsoff, light, moderate, aggressive, maximumoff

--format json returns an envelope wrapping the ExtractedDocument — the document lives under .result for extract and under .results[] for batch, with content, metadata, tables, and images as fields of that nested document. --format text prints just content. --content-format is what shows up inside that content field.

Decision tree

text
Who consumes the output?
├── LLM (Claude, GPT, Gemini, local) — embed/prompt context
│       --format text --content-format markdown
├── Vector store / RAG indexer
│       --format json --content-format markdown
│       (markdown preserves structure for chunking)
├── Downstream parser that expects machine-readable JSON
│       --format json --content-format plain
│       (cleanest text + structured metadata)
├── Human review / archival
│       --format text --content-format markdown
├── HTML re-rendering / web display
│       --format json --content-format html
├── Lossless intermediate for pandoc / academic tooling
│       --format json --content-format djot
└── Token-budget-constrained pipeline
        --format text --content-format plain
        (drops markup; add --token-reduction moderate for further savings)

Examples

Feed a PDF directly into an LLM:

bash
xberg extract paper.pdf --content-format markdown

Index a corpus into a RAG store with tables and headings preserved:

bash
xberg batch docs/*.pdf --format json --content-format markdown \
  | jq -c '.results[] | {content: .content, tables: .tables}'

Strip a file to bare text for a token-tight summarizer:

bash
xberg extract long.pdf \
  --content-format plain \
  --token-reduction moderate

Pull metadata only, ignore content:

bash
xberg extract file.pdf --format json | jq '.result.metadata'

When in doubt

  • Default to markdown as the content format. It is the best compromise across LLMs, RAG, and human review, and Xberg has the most faithful renderer for it.
  • Reach for plain only when downstream cannot tolerate any markup.
  • Reach for djot only if you're already in a djot/pandoc pipeline.
  • Reach for html only when re-rendering for the web.
  • Reach for json for a heading-driven content tree, or doctags for Docling-compatible output.

Token-reduction (orthogonal)

--token-reduction collapses whitespace, strips repeated headers/footers, and trims boilerplate. It composes with any --content-format:

  • off (default), light, moderate, aggressive, maximum.

Use moderate as a safe starting point for LLM context windows. maximum is lossy — verify before relying on it.

See references/cli-reference.md for the full flag set and references/configuration.md for the equivalent output_format and token_reduction keys in xberg.toml.

Frequently asked questions

What does the Picking A Format AI skill do?

Use when choosing an output format for extracted documents — plain text, markdown, djot, HTML, JSON, or DocTags. Maps consumer (LLM, parser, archive) to the right `--format` / `--content-format` pair.

Why use Picking A Format on TypingMind?

Because you install it once and use it with any model. Picking A Format 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 Picking A Format in TypingMind?

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

Which AI models can use Picking A Format?

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 Picking A Format?

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

Is the Picking A Format 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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