Core Components And Structured Io

Organization

VectorSpaceLab

core-components-and-structured-io

Build service-free AdalFlow Component, DataClass, Prompt, parser, and structured-output pipelines.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`core-components-and-structured-io`
Stars	341
Forks	26
Bundled files	5
License	MIT
Links	Skill source Repository

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.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Core Components And Structured Io AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/core-components-and-structured-io
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/core-components-and-structured-io

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/adalflow/sub-skills/core-components-and-structured-io .claude/skills/core-components-and-structured-io

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Core Components And Structured Io 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 Core Components And Structured Io 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 Core Components And Structured Io 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.

Core Components and Structured I/O

Use this sub-skill when a task needs AdalFlow's service-free building blocks:

Component, DataComponent, Sequential, nested component/parameter registration, state dictionaries, and lifecycle methods.
DataClass, required_field, JSON/YAML/schema/signature helpers, and nested dataclass serialization.
DataClassParser, string parsers, output parser selection, and Jinja Prompt rendering.
Small deterministic task-pipeline prototypes that do not call a live provider.

Do not use this sub-skill for live model calls, provider clients, or Generator internals; route those to model-client-and-generator-workflows. Route tool/agent/streaming work to agents-tools-and-streaming, and trainer/optimizer work to evaluation-and-optimization.

Load order

Read core API reference for verified signatures, lifecycle rules, object relationships, and routing boundaries.
Read structured I/O and parsers for copyable DataClass, parser, prompt, and mini-pipeline recipes.
If behavior is surprising, read troubleshooting before changing code.
For a deterministic environment check, run structured IO smoke or core component smoke. These scripts do not use provider credentials, network, datasets, or model calls.

Operating rules

Always call super().__init__() before assigning child Component or Parameter attributes in a custom Component.
Implement call for synchronous inference. Implement acall only when the component genuinely has async work. Implement forward or bicall only for training-mode behavior.
In train() mode, Component.__call__ expects a Parameter output; in eval() mode, it rejects Parameter outputs. DataComponent is intentionally non-trainable and dispatches directly to call.
Decorate every DataClass subclass with @dataclass; use field(metadata={"desc": ...}) or metadata={"description": ...} to make prompt schemas useful.
Prefer default_factory for mutable fields, and use required_field() when a required field must appear after optional fields.
For structured model outputs, generate prompt instructions with DataClassParser.get_output_format_str() and parse service-free strings with the same parser before connecting a Generator elsewhere.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Core Components And Structured Io AI skill do?

Build service-free AdalFlow Component, DataClass, Prompt, parser, and structured-output pipelines.

Why use Core Components And Structured Io on TypingMind?

Because you install it once and use it with any model. Core Components And Structured Io 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 Core Components And Structured Io in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adalflow/sub-skills/core-components-and-structured-io. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Core Components And Structured Io?

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 Core Components And Structured Io?

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

Is the Core Components And Structured Io AI skill free?

Yes. It is published on GitHub by VectorSpaceLab 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Core Components and Structured I/O

Load order

Operating rules

Bundled files

Frequently asked questions

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