Analyze Python code complexity for a file or directory. Returns per-function metrics: cyclomatic complexity (McCabe), cognitive complexity, nesting depth, function length, and letter grades (A-F).

Get an overall complexity health score (0-100) with letter grade (A-F), complexity profile classification, and top offenders. Quick project health check.

Detect unused imports, functions, variables, and arguments in Python code. Supports cross-module analysis to reduce false positives.

Scan Python code for security vulnerabilities: command injection, SQL injection, path traversal, hardcoded secrets, unsafe deserialization, XSS, and more. Maps findings to CWE identifiers.

Get an overall security health score (0-100) with grade, profile, and top vulnerability rules. Quick security posture assessment.

Analyze Python import dependency graph. Returns module metrics, circular dependencies (cycles), orphan modules, hub modules, and instability scores. Use to understand project architecture.

Get an import graph health score (0-100) with grade. Penalizes circular dependencies, orphan modules, unstable modules, and hub concentration. Quick architecture health check.

Detect code clones (duplicated code blocks) in a Python project. Finds Type-1 (exact), Type-2 (renamed), and Type-3 (near-miss) clones. Returns clone pairs with similarity scores and cluster analysis.

Analyze test suite quality: assertion density, test length, naming conventions, magic numbers, and more. Returns per-file and per-test metrics with a quality score (0-100) and grade.

Find code hotspots — files with high git churn AND high complexity. These are the riskiest files in a project: frequently changed AND hard to understand. Requires a git repository. Returns hotspot scores, risk levels, and churn/complexity breakdown.

Get overall hotspot health score (0-100) with grade. Measures concentration of risk (churn x complexity). Lower scores indicate more hotspots that need attention.

Audit Python project dependencies for outdated packages and known vulnerabilities. Reads requirements.txt and/or pyproject.toml. Returns per-dependency status, latest versions, and CVE/vulnerability details.

Analyze the ripple effect of changing specific files. Shows direct dependents, transitive impact, affected tests, and an impact score (fraction of project affected). Use before refactoring to understand blast radius.

Analyze module coupling metrics: afferent coupling (Ca), efferent coupling (Ce), instability (Ce/(Ca+Ce)), and hub scores. Identifies tightly-coupled modules that may resist change.

Analyze Python type annotation coverage: function signatures, parameters, return types, Any usage, and type: ignore comments. Returns per-file metrics and coverage percentages.

Get overall type coverage health score (0-100) with grade. Measures annotation completeness, Any usage, and type: ignore density.

Audit environment variable usage: find undefined vars referenced in code, unused vars in .env files, secrets in templates, and missing .env.example entries. Supports Python, JavaScript, and shell scripts.

Audit recent git commits in a repository. Extracts changed files and runs static analysis (security scan, complexity) on each commit. Returns per-commit grades, security findings, and complexity violations.

Get a quick overall grade and score for recent commits in a repo. Returns the aggregate score, grade, commit count, and security summary.

Check Python naming conventions (PEP 8). Detects violations: functions/methods must be snake_case, classes must be CamelCase, constants must be UPPER_SNAKE_CASE. Returns violations with suggestions.

Get a naming convention health score (0-100) with grade. Measures PEP 8 naming compliance: snake_case functions, CamelCase classes, UPPER_SNAKE_CASE constants.

Scan source code for technical debt markers: TODO, FIXME, HACK, XXX, BUG, NOTE, OPTIMIZE, REFACTOR comments. Returns items with file, line, tag, severity, and message. Optionally enriches with git blame metadata.

Get a technical debt health score (0-100) with grade based on density and severity of TODO/FIXME/HACK markers. Shows hotspot files.

Detect common Python semantic bugs using AST analysis. Finds 8 categories: missing f-strings, mutable class variables, late-binding closures, call-expression defaults (datetime.now()), mutable default arguments, assert-on-tuple, unreachable code, and unreachable exception handlers. Every finding indicates a likely real bug, not a style violation.

Get a bug detection health score (0-100) with grade (A-F), bug profile classification (clean/fstring_heavy/closure_heavy/etc.), and breakdown by rule and severity. Quick bug health check.

Audit Python docstring coverage and quality. Checks public functions, methods, classes, and modules for missing or low-quality docstrings. Returns per-entity coverage, missing docstrings, and quality issues.

Get a docstring coverage health score (0-100) with grade. Measures what percentage of public entities have docstrings and penalizes low-quality docstrings. Shows worst files.

Run all analyses (complexity + dead code + security + imports + clones + test quality + type coverage + env audit + naming + TODO debt + bug detection + docstring coverage) on a Python project and return a combined health report with scores, grades, and top issues. Note: hotspot, dependency, and change impact require additional context (git repo, requirements files, changed files) so are excluded from this scan.