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Specx Project Tooling

Community
maksimzayats
specx-project-tooling

Add strict Python project tooling for a specx service. Use when creating or updating `pyproject.toml`, `uv` dependency groups and lock checks, Ruff formatting and linting, mypy strict mode, pytest and HTTPX2 test configuration, Makefile commands, root `AGENTS.md` command guidance, or CI-like local guardrails.

Overview

Publishermaksimzayats
Repositoryspecx
Skill namespecx-project-tooling
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 Project Tooling 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-project-tooling
  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-project-tooling .claude/skills/specx-project-tooling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specx Project Tooling 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 Project Tooling 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 Project Tooling 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 Project Tooling

Use this skill to make the repository executable and checkable. Read references/tooling.md before editing tooling files.

Workflow

  1. Use specx init <path> for the fresh framework-neutral baseline. It renders the canonical pyproject.toml, Makefile, .python-version, health core/IOC scaffold, mirrored unit tests, and root guidance, then runs uv add specx diwire and uv add --dev mypy pytest ruff unless --no-sync is passed. Let uv select and record compatible releases rather than rendering dependency floors.
  2. Preserve the repo's Python version if it already exists. For a new repo, default to Python 3.14. The initializer accepts any major.minor value; do not hardcode a release allowlist that blocks future Python versions.
  3. Use uv metadata in pyproject.toml.
  4. For a new project, verify current compatible releases against the official package indexes and documentation before copying reference floors. Preserve intentional versions in an existing repo unless the user asks to upgrade.
  5. Add runtime dependencies only for real runtime features. For a starter API, include specx, FastAPI, diwire, pydantic-settings, and Uvicorn.
  6. Add dev dependencies for pytest, Ruff, mypy, HTTP testing, and ASGI lifespan testing when there is a FastAPI app.
  7. Keep mypy strict. Do not enable the DIWire mypy plugin for constructor-field injection; it is only needed if an existing project intentionally uses resolver_context.inject function wrappers.
  8. Enable select = ["ALL"] for Ruff in new projects. Ignore only formatter conflicts and deliberate file-category conventions, and put a concise comment describing what each ignored rule checks beside its code.
  9. Generate [tool.specx] with select = ["ALL"] so every applicable built-in guardrail is explicit. Missing technology surfaces are skipped for ALL; explicit technology selectors still warn when their surface is absent.
  10. Add simple Makefile targets: check, format, lint, test, and dev when there is a FastAPI delivery app. lint runs Ruff, mypy, and uv run --locked specx check; do not create separate guardrails or lock-check targets.
  11. If SQLAlchemy adapters exist, add Alembic dependency/config and migrate/makemigrations targets with $specx-sqlalchemy-migrations.
  12. When adding or changing Makefile targets, update root AGENTS.md Commands so coding agents run the right project commands.
  13. Use locked execution for non-mutating validation commands.
  14. Run the smallest useful checks after changing tooling.

Guardrails

  • Do not add heavyweight services such as Docker, Alembic, Redis, or PostgreSQL unless the repo has code that uses them.
  • Once a SQLAlchemy adapter exists, Alembic is required. Do not use metadata.create_all as a replacement for migrations.
  • Do not split linting rules across many files unless the project already does.
  • Keep commands copyable and non-interactive.
  • Keep AGENTS.md command guidance aligned with the actual Makefile. Do not list migration commands before SQLAlchemy/Alembic exists.

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/tooling.md - recommended pyproject.toml, Makefile, and command 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 Project Tooling AI skill do?

Add strict Python project tooling for a specx service. Use when creating or updating `pyproject.toml`, `uv` dependency groups and lock checks, Ruff formatting and linting, mypy strict mode, pytest and HTTPX2 test configuration, Makefile commands, root `AGENTS.md` command guidance, or CI-like local guardrails.

Why use Specx Project Tooling on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maksimzayats/specx/tree/main/.agents/skills/specx-project-tooling. 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 Project Tooling?

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 Project Tooling?

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

Is the Specx Project Tooling 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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