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Specx Diwire Composition

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maksimzayats
specx-diwire-composition

Wire dependency injection for a specx Python service with `diwire`. Use when adding `ioc/container.py`, explicit dependency registrations for capabilities, repositories, gateways, UoW managers, clients, settings, or factories, `Injected[...]` constructor fields, FastAPI app factory/lifecycle composition, test overrides, or rules that keep containers out of core use cases and services.

Overview

Publishermaksimzayats
Repositoryspecx
Skill namespecx-diwire-composition
Stars
201
Forks
34
Bundled files
2
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.

  • 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 Diwire Composition 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/maksimzayats/specx/tree/main/.agents/skills/specx-diwire-composition
  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/maksimzayats/specx.git /tmp/specx
mkdir -p .claude/skills
cp -r /tmp/specx/.agents/skills/specx-diwire-composition .claude/skills/specx-diwire-composition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specx Diwire Composition 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 Diwire Composition 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 Diwire Composition 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 Diwire Composition

Use this skill whenever object graphs, factories, controllers, adapters, or test overrides need diwire. Read references/diwire.md before writing DI code.

Rules

  • Application classes receive dependencies through constructor fields typed as Injected[DependencyType].
  • Keep injection constructor-based. Do not hide dependency resolution in application functions with resolver_context.inject.
  • Capabilities are normal injectable collaborators. Register only capability abstractions or existing instances that auto-wiring cannot infer.
  • Only ioc/, top-level delivery __main__.py/factory/lifecycle modules, and tests create or use diwire.Container.
  • Do not inject Container except in FastAPILifecycle, where it is used only to call container.aclose() during app shutdown.
  • Do not inject logging.Logger or register loggers in the container. Runtime logging is configured once by a top-level infrastructure configurator; classes that log create local stdlib loggers.
  • Do not call container.resolve() inside core use cases or services.
  • Let diwire auto-wire concrete project classes.
  • Let diwire auto-register BaseRuntimeSettings subclasses as root-scoped singleton factories. Register a settings instance only for an intentional test override or an explicitly prebuilt configuration object.
  • Register only abstractions, external adapter bindings, gateway implementations, factories, and instances that auto-wiring cannot infer. Keep registration in private _register_dependencies(...) inside ioc/container.py.
  • Register the container instance itself with provides=Container only for the FastAPI lifecycle shutdown dependency.
  • Register concrete gateway implementations for their core BaseGateway ports.
  • Register health readiness gateway implementations, such as SQLAlchemyReadinessCheckGateway, for ReadinessCheckGateway when /readyz needs infrastructure checks.
  • Register unit-of-work managers for UoW abstractions. Persistence use cases inject the manager and open the active UoW inside execute(...); they do not inject repositories, active UoWs, providers, SQLAlchemy sessions, engines, session factories, or concrete infrastructure adapters directly.
  • Unit tests receive a fresh native pytest container fixture built by the project's real get_container(). Put project-wide test overrides in that fixture and register scenario-specific overrides directly in the test before resolving the target.
  • Integration tests receive the transactional real-app container fixture. They must use the real internal graph; do not mock internal use cases, services, or capabilities.
  • Use container.resolve(...) for normal synchronous graph construction, even for targets with async methods; use await container.aresolve(...) only when DI construction itself has async providers.
  • One-off class-based test doubles live in the test_*.py module that uses them. Reused unit-test doubles may live in mirrored tests/unit/core/<scope>/{capabilities,gateways,repositories}/fake_<source_module>.py modules.
  • Inline MagicMock or AsyncMock in the test function when only one behavior needs to change.
  • Do not create DI-backed test factories, target harnesses, harness.py, tests/_support/fakes, tests/**/_fakes.py, or fake modules outside those mirrored unit port/capability packages.
  • Keep tests on native pytest fixtures. Do not enable diwire.integrations.pytest_plugin, and do not use Injected[...] parameters in test functions.
  • FastAPI async test helpers must enter LifespanManager and pass the yielded manager's state-aware manager.app to the HTTPX2 ASGITransport.

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/diwire.md - container code, registration examples, controller composition, and test override patterns.

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 Specx Diwire Composition AI skill do?

Wire dependency injection for a specx Python service with `diwire`. Use when adding `ioc/container.py`, explicit dependency registrations for capabilities, repositories, gateways, UoW managers, clients, settings, or factories, `Injected[...]` constructor fields, FastAPI app factory/lifecycle composition, test overrides, or rules that keep containers out of core use cases and services.

Why use Specx Diwire Composition on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maksimzayats/specx/tree/main/.agents/skills/specx-diwire-composition. 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 Diwire Composition?

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 Diwire Composition?

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

Is the Specx Diwire Composition 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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