Python Coder logo

Python Coder

Organization
sno-ai
python-coder

Implement and refactor production Python, FastAPI, Pydantic, async, and migrations.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill namepython-coder
Stars
857
Forks
609
Bundled files
2
LicenseApache-2.0
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 sno-ai on GitHub. Read the source before you install it.

Installation

Install the Python Coder 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/sno-ai/sno-station-skills/tree/main/plugins/sno-station-standalone/skills/python-coder
  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/sno-ai/sno-station-skills.git /tmp/sno-station-skills
mkdir -p .claude/skills
cp -r /tmp/sno-station-skills/plugins/sno-station-standalone/skills/python-coder .claude/skills/python-coder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Coder 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 Python Coder 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 Python Coder 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.

Python Coder

Every development task has a finite budget including setup, tests, and reporting. Before running tests, estimate duration from available timings and identify the changed behavior or directly affected consumer. If timing is unknown, state an estimate; do not build a timing system or run a preliminary suite. Full evaluation, unit, integration, or repository suites require the owner's explicit request, never a skill, PRD, review, or default command. Remove checks that neither prove the requested main function nor expose its defects. Repeat only after a relevant change or failure. Every new security check requires the owner's explicit approval for that specific check; remove auxiliary security, assertion, hash, or bookkeeping blockers of useful work.

Write production Python that fits the current repository, stays small, and is easy to verify. Local repo instructions add project-specific detail; they do not weaken these rules.

Runtime Source of Truth

Honor the Python version declared by the user or the repository (pyproject.toml requires-python, .python-version, setup.cfg, tox.ini, CI, package metadata). Never emit syntax, stdlib APIs, or typing features newer than the target. For 3.14+ targets, prefer 3.14 idioms where they improve clarity or correctness: lazy annotations (PEP 649), InterpreterPoolExecutor (PEP 734), t-strings (PEP 750), free-threading awareness (PEP 779).

Boundaries

  • No backward compatibility by default. No legacy paths, compatibility wrappers, migration shims, dual-format support, or fallback behavior unless explicitly requested. A published external contract is the only exception, and only when the user or repo instructions confirm it is published.
  • Read before writing. Relevant exports, immediate callers, shared utilities, nearby tests, and the package's typecheck/test setup — before adding code.
  • Official docs for third-party behavior. Before changing a dependency version, or using a third-party API, SDK, CLI, or service in a way neither the PRD nor existing code already shows, read its current official documentation; for a version change, read the release notes between the two versions. Name the page and version in the report. When a PRD governs the work and lacks or contradicts that fact, add it to the PRD's execution notes.
  • Surgical changes. Touch only what the task requires. No adjacent refactoring, no speculative abstractions or configurability, no error handling for scenarios that cannot happen.
  • Deterministic code for deterministic decisions. Routing, retries, status-code handling, schema validation, parsing, and transforms belong in code, never in a model call.
  • Conflicting local patterns: pick one source of truth (by authority, recency, test coverage), state the choice, flag the other for cleanup. Never blend conventions into a third style.

Design Rules

Intent: types make illegal states unrepresentable, data structures replace branches, and every line is a liability that must earn its place.

Types

  • Zero Any. Use object + narrowing, generics, or TypedDict. cast() only at validated boundaries, with a comment saying why.
  • Literal[...] over bare str; TypedDict over dict[str, Any] — split required/optional via a required base class + total=False subclass so guaranteed keys stay guaranteed.
  • Protocol over ABC (structural typing, stub-friendly testing); class hierarchies max 1 level — favor composition.
  • @dataclass(frozen=True, slots=True) for value objects with behavior; TypedDict for data crossing boundaries (JSON, APIs, configs).
  • T | None over Optional[T]; type X = ... aliases (3.12+); typing.Self for fluent/builder methods; @overload when the return type depends on an argument; ClassVar[T] for class-level state.
  • Abstract types (Sequence/Mapping/Iterable from collections.abc) in parameters, concrete types in returns. Explicit return types on public functions.
  • from __future__ import annotations: omit on 3.14+ targets and remove it from files you edit there; on older targets follow the repo's existing annotation strategy — do not remove it opportunistically.
  • if TYPE_CHECKING: for heavy or cycle-prone imports.

Structure & errors

  • Functions under ~50 lines, max 3 indentation levels, early-return guard clauses, happy path at lowest indentation.
  • Exception hierarchies for expected errors: a base class plus narrow subclasses, so callers choose their catch specificity. Wrap only the line that can raise; never bare except:; no mutable default arguments; no magic strings/numbers (constants or Literal).
  • Resources (connections, files, locks) always via context managers or finally.
  • __all__ declares each module's public API. Pure logic in the core, side effects at the edges.
  • Replace manual counting/grouping/nested combinatoric loops with Counter, defaultdict, itertools.
  • Docstrings are contracts (preconditions, silenced errors, external failure modes), not narration; skip when nothing is non-obvious.

Async & parallelism

  • asyncio.TaskGroup over gather() — structured cancellation, no orphaned tasks.
  • CPU-bound work: InterpreterPoolExecutor over ProcessPoolExecutor (3.14+); keep threads for I/O.
  • Shared mutable state is always locked (threading.Lock / asyncio.Lock) — free-threaded Python (PEP 779) turns previously-hidden races into real bugs.

Boundaries & config

  • Keep fixed-format parsing and protocol literals in code; use existing translation resources only for text that requires localization.
  • Reuse the repository's configuration and boundary-validation approach; do not introduce a configuration framework, translation system, or startup gate for an ordinary change. Internal calls trust types.
  • SQL always via parameterized queries, HTML through the framework's escaping, log fields via extra={} — never hand-interpolated strings. T-strings (string.templatelib, 3.14+) do no escaping by themselves; use them only where the repo already has a vetted template processor for that exact sink.

Logging

  • Never print() in production code. logging.getLogger(__name__); %s formatting or extra={} — never f-strings in log calls; logger.exception for stack traces.

Verification

Before reporting done, every claim must trace to a command actually run this session:

  1. The repo's declared type checker passes with zero errors on changed files; no typing features newer than the declared runtime.
  2. Run the repo's declared check/fix command and report zero remaining errors.
  3. Search changed files for Any — confirm none; confirm annotation strategy matches the target runtime.
  4. Re-read the diff once: delete anything removable without losing correctness or clarity, and confirm every changed line traces to the request.

If a check fails, report the failure with its output — never report done with errors outstanding.

For advanced patterns (generic protocols, async generators, thread-safe singletons, custom decorators, sentinel objects), read references/advanced-patterns.md.

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 Python Coder AI skill do?

Implement and refactor production Python, FastAPI, Pydantic, async, and migrations.

Why use Python Coder on TypingMind?

Because you install it once and use it with any model. Python Coder 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 Python Coder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sno-ai/sno-station-skills/tree/main/plugins/sno-station-standalone/skills/python-coder. 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 Python Coder?

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 Python Coder?

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

Is the Python Coder AI skill free?

Yes. It is published on GitHub by sno-ai under the Apache-2.0 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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