strictify

A Claude Code plugin that applies opinionated Python code quality enforcement to any repo.

Run /strictify in any Python project. It analyzes what's already in place, proposes strictness additions across 21 categories, and applies approved changes — including self-reinforcing hookify rules that capture your taste preferences as you work.

Install

claude plugins add github:crypdick/strictify

Or add to ~/.claude/settings.json:

{
  "extraKnownMarketplaces": {
    "strictify": {
      "source": { "source": "github", "repo": "crypdick/strictify" }
    }
  },
  "enabledPlugins": {
    "strictify@strictify": true
  }
}

What it does

/strictify runs a three-phase workflow:

1. Analyze — scans pyproject.toml, pre-commit config, package layout, Python version, package manager, test setup, beartype, domain structure
2. Propose — presents 21 categories grouped into 6 areas, showing current state vs. proposed change for each. You veto what you don't want.
3. Apply — merges configs, copies scripts, installs hooks, adds dev dependencies

Categories

Group	Categories
Static Analysis & Type Safety	Pre-commit framework, Ruff, mypy strict, Beartype, Semantic typing (NewType), Parse-don't-validate
Code Health	Vulture (dead code), Xenon (complexity), Pyupgrade + Flynt, Structured logging
Testing & Coverage	Coverage fail_under=100, Fast tests (xdist, timeouts, --failed-first)
Architecture & Organization	File length limits, Architectural layers, Quality scorecard
Environment & Infrastructure	Ephemeral environments, Per-worktree isolation
Ongoing Enforcement	Custom hooks, Hygiene hooks, Doc gardening, Taste enforcer

Hookify rules

Four rules are installed into your project's .claude/ directory:

• taste-enforcer — when you express a coding preference ("don't use X", "always prefer Y"), Claude codifies it as a pre-commit hook, hookify rule, or pyproject.toml setting
• no-junk-drawers — warns on utils.py, helpers.py, misc.py — name modules after what they do
• parse-dont-validate — nudges toward boundary parsing with constrained types instead of scattered validation
• semantic-types — catches bare str/int for domain concepts like user_id, nudges toward NewType

Custom pre-commit hooks

Five scripts are adapted to your repo and installed in scripts/pre_commit_hooks/:

Hook	What it catches
check_exception_handling.py	Bare except:, swallowed exceptions, except Exception: pass
check_print_statements.py	print() in production code, unstructured logging (f-strings/concat in logger calls)
check_file_length.py	Files over 400 logical lines
check_timeless_comments.py	Temporal language in comments ("legacy", "old", "deprecated")
fix_future_annotations.py	Misplaced from __future__ import annotations

All hooks output {file}:{line}: {message} — {remediation} so both humans and AI agents can act on violations.

Philosophy

Inspired by AI Is Forcing Us to Write Good Code and Harness Engineering:

• Enforce taste, not arbitrary strictness — every rule exists because it improves code quality
• Bias strict, but check in — aggressive defaults, user vetoes what doesn't fit
• Self-reinforcing — hookify rules capture new preferences as you express them
• Parse, don't validate — coerce at the boundary, carry proof through types
• Agent legibility — make code navigable by both humans and AI agents

License

MIT