Claude-skill-registry decomplect-py

Architectural code analysis for Python design quality. Evaluates simplicity (Rich Hickey), functional core/imperative shell (Gary Bernhardt), and coupling (Constantine & Yourdon). Use for design review or architectural assessment of Python code.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/decomplect-py" ~/.claude/skills/majiayu000-claude-skill-registry-decomplect-py && rm -rf "$T"

manifest: skills/data/decomplect-py/SKILL.md

Decomplect-Py

Architectural analysis for Python design quality.

Usage

/decomplect-py                # Run all 3 analyzers in parallel
/decomplect-py --simplicity   # Specific analyzer
/decomplect-py --fcis         # Specific analyzer
/decomplect-py --coupling     # Specific analyzer

Analyzers

Analyzer	Question
simplicity-analyzer	Is this truly simple or just easy?
fcis-analyzer	Is pure logic separated from I/O?
coupling-analyzer	Are modules well-separated?

What It Checks

Pillar	Focus
Simplicity	Values over state, decomplected concerns
FCIS	Functional core (pure), imperative shell (I/O)
Coupling	High cohesion, low coupling

When to Use

Reviewing Python system design
Before major refactoring
Assessing architectural quality
Checking if code is "Rich Hickey approved"

Supported Languages

Python (
```
.py
```
files only)

Python-Specific Patterns

```
dataclasses(frozen=True)
```
and
```
NamedTuple
```
for immutable values
Mutable default argument anti-patterns
Django/Flask view architecture
Circular import detection
Module-level state analysis