sloplintpy 0.3.1

Fast, deterministic linter for AI 'slop' in Python — runs right after Ruff.

sloplint

A fast, deterministic, no-LLM linter that counters AI slop in Python — a deliberately nitpicking, opinionated layer that runs right after Ruff in the same CI job. Ruff handles standard linting; sloplint adds the strict, slop-specific judgments Ruff intentionally won't ship, and never re-checks anything Ruff already covers.

Written in Rust, reusing Ruff's own parser crates for a full-fidelity AST + token stream.

Features

Rules that flag slop patterns no mainstream linter covers today. Stable rules run by default; preview rules are heuristic — enable them with --preview.

Rule	Stability	What it flags
SLP010	stable	Comments — banned by default (relax per-path in sloplint.toml)
SLP020	stable	Cross-file duplicate / near-duplicate functions — copy-paste and "same logic, slightly different"
SLP030	stable	Overly defensive try/except
SLP050	stable	Non-ASCII source (e.g. emoji)
SLP080	stable	Oversized files (default: > 400 lines, configurable via file_max_lines)
SLP082	stable	Deep control-flow nesting inside a function (default: > 4 levels, via nesting_max_depth)
SLP090	stable	Flat-directory fanout — too many .py modules in one directory (default: > 15, via dir_max_modules)
SLP001	preview	Redundant "what" comments that just restate the code
SLP002	preview	Redundant docstrings that just restate the code
SLP040	preview	Redundant type hints
SLP060	preview	Verbose, mechanical identifier naming
SLP084	preview	Deeply nested data-structure literals (a dict-of-lists-of-dicts blob past a depth — model it with a named type)
SLP120	preview	Low-cohesion "god classes" via LCOM4 (methods that split into unrelated groups)
SLP180	preview	Undeclared third-party imports — a module imported but missing from the project's pyproject.toml/requirements*.txt (broken on a clean install)

Plus software-quality metrics (cyclomatic + cognitive complexity, LCOM4 cohesion) with McCabe risk tiers, shields badges, and a per-PR summary — and package/module architecture metrics over the import graph (dependency cycles, coupling/instability, propagation cost, modularity) — via the metrics command and the GitHub Action.

Installation

sloplint ships on PyPI as sloplintpy (the wheel bundles the native binary — no Rust toolchain needed). The installed command is sloplint.

Run it directly with uvx (the package and command differ, so use --from):

uvx --from sloplintpy sloplint check    # Lint all files in the current directory.
uvx --from sloplintpy sloplint metrics  # Report software-quality metrics.

Or install sloplintpy with uv (recommended), pip, or pipx — then run sloplint:

# With uv.
uv tool install sloplintpy@latest   # Install the `sloplint` command globally.
uv add --dev sloplintpy             # Or add it to your project.

# With pip.
pip install sloplintpy

# With pipx.
pipx install sloplintpy

Usage

Once installed, sloplint is a native binary on your PATH:

sloplint check path/to/code              # lint (exit 1 on findings)
sloplint check src --format sarif        # SARIF / json / github / text
sloplint metrics src                     # software-quality metrics table
sloplint metrics src --format github     # PR-summary markdown (CC risk tiers)
sloplint metrics src --format packages   # per-package feed: coupling, cycles, abstractness (JSONL)
sloplint metrics src --max-cyclomatic 10 # CI gate: exit 1 over McCabe's ceiling
sloplint metrics src --badges badges/    # emit SVG + shields-endpoint badges
sloplint init                            # wire sloplint into your AI coding tool (see below)
sloplint parse file.py                   # dump AST + tokens (debug aid)

From a clone, run it through cargo instead (cargo run -p sloplint -- check path/to/code), or build a wheel locally with maturin (maturin build --release).

Comments are banned by default; relax per-path (see Configuration). Preview rules need --preview.

Agent-loop integration

sloplint is fast, deterministic and reproducible — so instead of only catching slop in CI, after the code has landed, you can run it inside your AI coding tool's edit loop. The tool fires a hook after every file edit, sloplint checks the just-edited file, and any findings go straight back to the agent so it self-corrects in the same turn — a guardrail, not just a gate.

sloplint init                 # detect the tools in this repo and wire them up
sloplint init --tool claude   # or target one: claude | cursor | aider | all
sloplint init --dry-run       # preview the config changes without writing

init writes (merging into any existing config, never clobbering it):

Tool	Config	Mechanism
Claude Code	.claude/settings.json	PostToolUse hook → sloplint check --hook --format agent
Cursor	.cursor/hooks.json	afterFileEdit hook → sloplint check --hook --format agent
Aider	.aider.conf.yml	lint-cmd: "python: sloplint check --format agent"

The Claude Code and Cursor hooks pass the edited path as JSON on stdin; check --hook reads it (no jq needed), lints just that file with the fast per-file rules, prints any findings to stderr in the terse path:line:col: CODE message agent format, and exits 2 so the agent sees them. A clean edit exits 0 silently. Whole-project rules (clone detection, dir fanout, undeclared imports) still belong in the CI run — they need the whole tree, not one edit.

You can use the agent format anywhere, not just in hooks: sloplint check src --format agent.

Configuration

sloplint reads sloplint.toml, discovered from the working directory upward (or pass --config <path>). Every key is optional — the defaults are shown below.

ignore = ["SLP040"]           # turn specific rules/prefixes off
select = []                   # force-enable rules/prefixes
preview = false               # enable preview rules (same as --preview)

[limits]                      # thresholds for the size/structure rules
file_max_lines = 400          # SLP080
nesting_max_depth = 4         # SLP082
data_nesting_max_depth = 3    # SLP084
max_identifier_words = 4      # SLP060
dir_max_modules = 15          # SLP090
lcom4_max_components = 1       # SLP120 — flag a class that splits into > 1 cohesion group
lcom4_min_methods = 3         # SLP120 — skip classes smaller than this

[clone]                       # SLP020 near-duplicate detection
min_statements = 3            # ignore tiny functions
similarity = 0.85             # Jaccard similarity at/above which a pair is reported

[imports]                     # SLP180 undeclared third-party import
extra = []                    # extra distribution names to treat as declared (suppress FPs)

[badges]                      # which `metrics --badges` files to emit (see Metrics & badges)
# include = ["cyclomatic-risk"]   # per-metric badges; omit = all, [] = none
summary = []                  # metrics to fold into one combined `sloplint` badge

[[overrides]]                 # relax rules for matching paths (gitignore-style globs)
path = "tests/**"
ignore = ["SLP010"]
allow_comments = true         # permit comments here (otherwise banned)

Metrics & badges

Beyond the lint rules, sloplint metrics reports software-quality metrics — cyclomatic and cognitive complexity (with McCabe risk tiers), average function length, max nesting, comment density, type-hint coverage, and docstring coverage. These are measured, not linted, so they never duplicate Ruff. Gate them in CI by exit code (each names the offending functions and exits 1):

sloplint metrics src --max-cyclomatic 10   # fail if any function's cyclomatic complexity > 10
sloplint metrics src --max-cognitive 15    # ditto for SonarSource cognitive complexity

Docstring coverage is tracked separately from comment density, because the two measure different things: comment density counts #-comments, while many codebases document almost entirely via docstrings (a StringLiteral, not a Comment). The --format json rollup reports docstring_coverage (public defs/classes with a docstring ÷ all public defs/classes — "public" = not _-prefixed) and docstring_code_ratio (function docstring lines ÷ function NCSS). Low coverage flags an under-documented public API; a high ratio flags AI over-documentation — a verbose docstring stacked onto a one-line body. The --format functions / --format classes feeds carry has_docstring + docstring_lines per unit.

--badges badges/ writes an SVG + a shields.io endpoint JSON for each metric (cyclomatic-risk, max-cognitive, avg-function-loc, max-nesting, comment-density, docstring-coverage, …) — for example:

Choose which badges via [badges] in sloplint.toml: include picks the per-metric badges (omit the key for all, [] for none), and summary folds a list of metrics into one combined sloplint badge colored by the worst tier — e.g. include = [] + summary = [...] emits only:

Commit the SVGs, or host the *.json and point a shields URL at it for a badge that updates itself. The GitHub Action writes them when you set its badges-dir input.

Type-hint coverage

--format functions rows carry per-function annotation counts (typed_params, annotatable_params, has_return_annotation), and --format json rolls them up into param_annotation_coverage (annotated ÷ annotatable params) and fully_annotated_function_rate (functions with every param and the return type annotated). Annotatable params exclude the self/cls receiver and *args/**kwargs. This measures under-annotation as a quality concern (missing types are harder to read and refactor, and weaken tooling) — the bad direction is low coverage only. Fully-typed code is neutral-to-good and is never itself a slop signal.

Class metrics

--format classes emits one JSONL row per class — the class-level discovery feed: loc, methods, attributes, lcom4 cohesion (SLP120), is_abstract, and the two CK class metrics (Chidamber & Kemerer 1994):

• wmc — Weighted Methods per Class: the sum of the cyclomatic complexity of the class's direct methods. A class-weight measure that separates 40 trivial accessors from 40 branchy ones, where a raw method count can't.
• dit — Depth of Inheritance Tree: the longest path up to a root through first-party bases. Bases that resolve to object, the stdlib, or a third party are invisible and end the chain, so dit is a deliberate, conservative under-count of the true Python MRO depth.

--format json adds the matching aggregates next to the complexity figures: classes, max_wmc, avg_wmc, max_dit, avg_dit. Like the rest, these are descriptive distributions for tracking a repo over time, not pass/fail gates.

Package & module architecture metrics

sloplint metrics also analyzes the project's first-party import graph — the metrics the literature ties most directly to architectural decay, and the ones AI-generated codebases tend to do worst (circular imports, god-modules, flat dumping-grounds, hidden coupling). All deterministic and reproducible — no LLM, no randomness. Two feeds:

• --format packages — one JSONL row per package (directory): modules, loc, efferent / afferent coupling (ce / ca) and Martin instability, abstractness + distance from the main sequence, whether it sits in a dependency cycle (in_cycle), and the first-party packages it imports / is imported_by. The per-package discovery feed, mirroring --format functions / --format classes.

• --format json — a per-project packages rollup alongside the complexity figures:

  "packages": {
    "modules": 412, "packages": 37, "module_edges": 689, "package_edges": 81,
    "cycles": {            // cyclic dependency tangles (Tarjan SCC) — 2–11× defect density
      "tangles": 3, "largest_tangle": 9, "modules_in_cycles": 21,
      "pct_modules_in_cycles": 0.051,
      "runtime_tangles": 2,   // dropping `if TYPE_CHECKING:`-only edges (benign at runtime)
      "members": [["pkg.a", "pkg.b", "pkg.c"]]
    },
    "propagation_cost": 0.18, // how far a change ripples (DSM transitive-closure density)
    "modularity": {           // Newman–Girvan Q: declared packages vs. detected communities
      "q_declared": 0.41, "communities_declared": 37,
      "q_detected": 0.55, "communities_detected": 29,
      "gap": 0.14             // large positive gap ⇒ "packages in name only"
    }
  }
  

These are research-backed structural signals (Martin's package metrics; MacCormack's propagation cost; Newman–Girvan modularity; Melton & Tempero on cyclic dependencies) — descriptive measures for tracking a repo over time or comparing across codebases, not pass/fail gates. (Published clean-vs-slop reference distributions are the job of the benchmark harness, #55.)

Static test proxies (NOT coverage)

--format json also reports a test_proxies block — two static signals of how (un)tested a codebase is, computed without running anything:

"test_proxies": {
  "_note": "Static proxies, NOT coverage. Descriptive cohort statistics only — never a pass/fail gate. ...",
  "test_files": 12, "production_files": 48,
  "test_loc": 1840, "production_loc": 5210,
  "test_code_ratio": 0.353,    // test LoC / production LoC
  "test_functions": 96, "assertions": 311,
  "assertion_density": 3.24    // assertions per test function (asserts + self.assertX +
                               // pytest.raises + self.fail), null when there are no test fns
}

Test files are identified by path (test_*.py, *_test.py, a tests/ segment, conftest.py); the figures also appear in the text table and the --format github PR summary.

[!IMPORTANT] This is not test coverage. Real coverage requires executing the tests, which a static linter cannot do. These are proxies: low test:code ratio + low assertion density suggest under-testing, but they cannot tell a shallow test from a thorough one — a test can carry many asserts and verify nothing, or few asserts and be excellent. So they are reported as descriptive cohort statistics and are never a pass/fail gate. Their value is across a cohort (slop tends to ship far less test code with shallower assertions), not as a per-repo verdict. They are the cohort-level counterpart to the per-file SLP070 (assertion-free tests) and SLP160 (test mirroring) rules.

GitHub Action

Run sloplint on every PR — it uploads SARIF (inline annotations), posts a findings summary comment, and can emit metric badges:

permissions:
  contents: read
  security-events: write
  pull-requests: write
jobs:
  sloplint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: galthran-wq/sloplint@main
        with:
          paths: src
          badges-dir: .sloplint-badges   # optional

Intended to run after Ruff in the same job. See action.yml for all inputs.

Required permissions: security-events: write (SARIF upload) and pull-requests: write (PR comment) — both shown above. Without them the action degrades gracefully (it warns rather than failing).

By default the action downloads a prebuilt binary for the runner (set version: to a release tag like v0.2.0, or latest); if none is available it builds from source. Cut a release to publish binaries:

git tag v0.2.0 && git push origin v0.2.0   # triggers .github/workflows/release.yml

Layout

Crate	Role
sloplint	CLI binary
sloplint_linter	all rules + core run logic (cf. ruff_linter)
sloplint_python	parser seam over the pinned ruff_* crates
sloplint_diagnostics	rule-independent diagnostic model
sloplint_clone	near-duplicate function detection
sloplint_metrics	quality metrics, import-graph architecture metrics, badges
sloplint_report	output formatters (text/JSON/SARIF/markdown)
sloplint_dev	development utilities (cf. ruff_dev)