Specx Foundation

Community

maksimzayats

specx-foundation

Use specx foundation bases in Python services, or add a tiny project-local `foundation/` base for missing categories or stateful framework bases.

Overview

Publisher	maksimzayats
Repository	`specx`
Skill name	`specx-foundation`
Stars	201
Forks	34
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

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

Installation

Install the Specx Foundation 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/maksimzayats/specx/tree/main/.agents/skills/specx-foundation
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.

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/maksimzayats/specx.git /tmp/specx
mkdir -p .claude/skills
cp -r /tmp/specx/.agents/skills/specx-foundation .claude/skills/specx-foundation

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

Use it in TypingMind

Enable Specx Foundation 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 Specx Foundation 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 Specx Foundation 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.

specx Foundation

Use this skill when a service class needs an explicit base, or when a new class category may need a project-local foundation base. Read references/foundation.md before editing foundation-related code.

Workflow

Prefer packaged scoped bases from specx.core.foundation, specx.delivery.foundation, and specx.infrastructure.foundation.
Do not create an empty src/<package>/foundation/ package.
Add a project-local foundation/ module only when current code needs a real project-local base category or a stateful framework base that must not be shared globally, such as a SQLAlchemy declarative base.
Keep project-local foundation limited to base definitions: marker bases, common external-base wrappers, justified ABCs, and stateful framework bases. Put stable cross-scope concrete primitives under shared/, not foundation/.
Give every project source class, including each local base, a docstring that explains what it protects and includes a concrete usage example.
Make every non-foundation project source class inherit an explicit scoped base.
Name each class with the suffix implied by its base ancestry, such as TaskDTO, TaskEntity, TaskResponseSchema, CreateTaskUseCase, or TaskTitleNormalizerService.
Use BaseLifecycle for delivery app lifespan managers such as FastAPILifecycle.
Prefer @dataclass(frozen=True, kw_only=True, slots=True) for commands, queries, DTOs, entities, and other core data classes unless the user asks for another model type.
Keep BaseCommand and BaseQuery as use-case input bases, independent from BaseDTO. Commands and queries are not result DTOs.
When an application value has a limited known set, model it with BaseStrEnum instead of plain str or Literal[...].
Keep business rules, delivery behavior, adapter code, and runtime wiring out of project-local foundation modules.

Code Style

Use blank lines as logical separators in all code. Keep related statements together, but separate independent setup, action, assertion, response, branch, and transformation groups so long blocks stay readable.

References

references/foundation.md - packaged base catalog, import paths, extension rules, and architecture guardrails.

Bundled files

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

agents/openai.yaml1 KB
references/foundation.md12 KB

Frequently asked questions

What does the Specx Foundation AI skill do?

Use specx foundation bases in Python services, or add a tiny project-local `foundation/` base for missing categories or stateful framework bases.

Why use Specx Foundation on TypingMind?

Because you install it once and use it with any model. Specx Foundation 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 Specx Foundation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maksimzayats/specx/tree/main/.agents/skills/specx-foundation. 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 Specx Foundation?

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 Specx Foundation?

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

Is the Specx Foundation AI skill free?

Yes. It is published on GitHub by maksimzayats 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

specx Foundation

Workflow

Code Style

References

Bundled files

Frequently asked questions

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