Atlas / Skills / fcakyon / Python Guidelines

Python GuidelinesSAFE

skills/fcakyon/python-guidelines

Battle-tested Claude Code, OpenAI Codex, Cursor configs, plugins, hooks and agents with Kimi, MiniMax and GLM API support.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
Apache-2.0
Stars
1,174
01

Overview

Battle-tested Claude Code, OpenAI Codex, Cursor configs, plugins, hooks and agents with Kimi, MiniMax and GLM API support.

Read from source at commit ae618ef5da66OBSERVED · 2026-10-12
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: python-guidelines
description: This skill should be used when writing, reviewing, or refactoring Python code. Covers code integration, idiomatic patterns, docstring formatting, anti-abstraction rules, and software engineering basics.
---

# Python Guidelines

**Integrate into existing code. Don't append to it.**

> Simple is better than complex. Flat is better than nested.
> Errors should never pass silently. Unless explicitly silenced.
> If the implementation is hard to explain, it's a bad idea.
>
> -- The Zen of Python (PEP 20)

## Code Philosophy

- Match existing naming, importing, and signature patterns. Use existing utilities and data structures.
- Functions have a single purpose. Don't hardcode behavior that makes them less general.
- No trivial wrappers for 2 lines or less. Inline it.
- Inline single-use variables at the usage site.
- No try/except unless critical. Let errors surface.
- No duplicate code.
- Functions handle their own input validation. No if-else checks in main.
- Use pathlib, not os.path.
- Consider API and time costs for MongoDB/Gemini/OpenAI/Claude/Voyage.

Don't do this:

```python
# Generate comment report only if requested
if include_comments:
    comment_report = generate_comments_report(start_date, end_date, team, verbose)
else:
    comment_report = ""
    print("   Skipping comment analysis (disabled)")
```

Do this:

```python
comment_report = generate_comments_report(start_date, end_date, team, verbose) if include_comments else ""
```

Ask yourself: "Am I adding code, or integrating into what exists?"

## Simplicity Over Abstraction

**YAGNI: You Aren't Gonna Need It.**

Don't build for hypothetical future requirements. Add complexity only when the current task demands it.

Avoid:

- Abstract base classes for a single implementation
- Configuration options nobody asked for
- Error handling for impossible scenarios
- Wrapper classes around a single function
- Dependency injection when direct calls work
- Generic type parameters for one concrete type

Three similar lines of code is better than a premature abstraction. Refactor when the third real use case appears, not before.

But simplicity does not mean chaos. Always maintain:

- Clear function names that describe what they do
- Logical grouping of related code into modules
- Consistent naming conventions across the project
- Clean separation between I/O and logic
- Explicit parameters over global state or side effects

Ask yourself: "Is this abstraction solving a problem I have right now, or one I'm imagining?"

## Environment

- **Package manager**: uv (NOT pip)
- **Virtual env**: `source .venv/bin/activate` or `uv run python -c "..."`
- **3rd party packages**: Find source with `python -c "import pkg; print(pkg.__file__)"`, then Read.

## Testing Discipline

Never assume anything. Run `python -c "..."` to verify hypotheses about code behavior, package functions, or data structures before suggesting a plan or exiting plan mode.

Ask yourself: "Did I verify this with `python -c` before building on it?"

## Google-Style Docstrings

- **Summary**: Imperative mood ("Calculate", not "Calculates")
- **Args**: All parameters with types and descriptions. No default values. Indent 4 spaces.
- **Types**: `int | str` unions, uppercase shapes `(N, M)`, lowercase builtins `list`/`dict`/`tuple`, capitalize `Any`/`Path`
- **Optional**: `name (type, optional): Description`
- **Returns**: Always `(type)` in parentheses. Never tuple types. Separate named values for multiple returns.
- **Sections**: Examples (>>>), Notes, References (plaintext only). Section titles at 0 indent.
- **Omit**: "Returns:" if nothing returned, "Args:" if no args, "Raises:" unless critical
- **Classes**: Attributes section only, omit Methods/Args. Don't convert single-line to multiline.
- **`__init__`**: Args only. No Examples/Notes/Methods/References.
- **Tests**: Single-line docstrings only.
- Erase default values from existing arg descriptions. Optionally include minimal Examples.

Ask yourself: "Would a new developer understand this function from the docstring alone?"

## Reference Files

Read the matching file before you write the code, not after:

- [`references/idiomatic-patterns.md`](references/idiomatic-patterns.md) -- read when writing loops, comprehensions, unpacking, context managers, or dataclasses. 18 idioms with before/after code
- [`references/zen-of-python.md`](references/zen-of-python.md) -- read when choosing between two designs or judging whether an abstraction earns its place. PEP 20 with annotations
- [`references/google-style-guide.md`](references/google-style-guide.md) -- read when deciding on exceptions, mutable defaults, import style, naming, or comments
- [`references/effective-python-tips.md`](references/effective-python-tips.md) -- read when reviewing or refactoring existing code. Key tips from "Effective Python" (Brett Slatkin)
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (3)

LOWInventory / provenance · inv.symlink · CWE-1104
.claude/output-styles/adhd-explanatory.md
.claude/output-styles/adhd-explanatory.md
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
AGENTS.md
AGENTS.md
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
GEMINI.md
GEMINI.md
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-12 · audit v0.4.1 · source sha ae618ef5da66full audit observations/trust-audit/skill/fcakyon__python-guidelines.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-12ae618ef5da66SAFEB89first audit
05

Questions

What does the Python Guidelines skill do?

Battle-tested Claude Code, OpenAI Codex, Cursor configs, plugins, hooks and agents with Kimi, MiniMax and GLM API support.

Is Python Guidelines safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Python Guidelines access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (ae618ef5da66), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.

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