pyneat-cli 3.2.3

AI-Generated Code Scanner — detects bugs, security vulnerabilities, and quality issues that AI coding assistants introduce

PyNeat: AI-Generated Code Cleaner

PyNeat 3.1.7 is a code scanning and cleanup tool built specifically for AI-generated code. Unlike generic linters, PyNeat targets the patterns that AI coding assistants systematically produce -- phantom packages, hallucinated parameters, resource leaks, OWASP vulnerabilities, AI-specific security risks -- and cleans them up automatically. Supports 9 languages.

What It Does

AI assistants are fast — but they generate code with predictable problems:

• Phantom imports — generic names like utils, helpers, ai that don't exist
• Fake parameters — param1=x, fake=True, dummy_arg that do nothing
• Resource leaks — open() without with, requests without timeout
• Boundary errors — list[0] without empty check, .split()[0] without validation
• Redundant I/O — same API call 3+ times with identical arguments
• OWASP Top 10 — command injection, SQL injection, pickle RCE, weak crypto
• AI-specific risks — prompt injection, system prompt leakage, hallucinated API calls, tool call collisions
• Debug artifacts — print(), pdb, console.log left behind
• Naming chaos — mixed camelCase/snake_case in the same file
• Identity comparisons — is 200 instead of == 200
• Type checks — type(x) == list instead of isinstance(x, list)

PyNeat detects all of these and auto-fixes what it safely can.

Multi-Language Support

PyNeat handles 9 languages natively:

Language	Auto-fix	Security scan
Python	✅	✅
JavaScript	✅	✅
TypeScript	✅	✅
Go	✅	✅
Java	✅	✅
Rust	✅	✅
C#	✅	✅
PHP	✅	✅
Ruby	✅	✅

For maximum speed on large multi-language codebases, enable the Rust backend.

Quick Start

# Install
pip install pyneat

# Scan for issues
pyneat check your_file.py

# Clean AI-generated code patterns
pyneat clean your_file.py --dry-run --diff

# Auto-fix (with backup)
pyneat clean your_file.py --in-place --backup

For Python API usage and examples, see docs/quickstart.md.

Running pyneat without installation

If you are working from the source repository (e.g., after git clone or pip install -e .), you can run pyneat as a Python module without needing to install it:

# Run CLI commands via module invocation
python -m pyneat check your_file.py
python -m pyneat clean your_file.py --dry-run

Both pyneat check (after install) and python -m pyneat check (from source) are equivalent.

3-Tier Package System

PyNeat uses three packages to balance safety vs. aggressiveness:

Package	Use when
safe (default)	You want zero-risk fixes. Always-on rules that won't break code.
conservative	You want cleaner code. Adds unused import removal, f-string conversion, debug cleanup.
destructive	You want a full sweep. Aggressive refactoring — review changes before committing.

Safe Package (Default)

Runs automatically, no flags needed:

Rule	What it fixes
IsNotNoneRule	x != None → x is not None (PEP8)
RangeLenRule	range(len()) anti-pattern
TypingRule	Missing type annotations
CodeQualityRule	Magic numbers, empty except blocks
PerformanceRule	Inefficient loops
SecurityScannerRule	os.system, pickle, secrets, command injection, weak crypto

Conservative Package

pyneat clean your_file.py --package conservative

Adds: unused import removal, .format() → f-string, @dataclass suggestions, magic number detection, safe debug cleanup (--safe-debug-clean).

Destructive Package

pyneat clean your_file.py --package destructive

Adds: import rewriting/reordering, naming convention enforcement (PascalCase), nested if flattening (Arrow Anti-pattern), TODO/FIXME removal, redundant expression simplification, dead code removal, --aggressive-clean (strip ALL print() calls), --enable-all for all rules at once.

Security Scanning

SecurityScannerRule runs in all packages automatically.

Core Security Rules (SEC-001 to SEC-060)

Vulnerability	Auto-fix
yaml.load() without Loader	→ SafeLoader
Empty except: pass	→ raise
Command injection (os.system, subprocess shell=True)	Warning
SQL injection (string concatenation)	Warning
pickle.loads() (RCE risk)	Warning
eval/exec dynamic execution	Warning
Weak crypto (random for tokens, md5/sha1)	Warning
Hardcoded secrets (api_key, password)	Warning
Template injection (render_template_string)	Warning
Path traversal (open() with user input)	Warning
XXE (unsafe XML parsing)	Warning
Debug mode (DEBUG=True)	Warning
LDAP injection	Warning
SSRF / Open redirect	Warning
CORS misconfiguration	Warning

NEW Security Rules (SEC-061 to SEC-072)

Rule ID	Vulnerability	Severity	Description
SEC-061	Missing Subresource Integrity (SRI)	Medium	External <script>/<link> without integrity attribute
SEC-062	Missing Content-Type Validation	High	File upload without Content-Type verification
SEC-063	Missing Rate Limiting	Medium	Sensitive endpoints without rate limiting
SEC-064	Weak JWT Secret Key	Critical	Weak or hardcoded JWT secret
SEC-065	Incomplete Session Destruction	Medium	Logout without full session cleanup
SEC-066	Timing Attack Vulnerability	Medium	== used instead of timing-safe comparison
SEC-067	Weak Server-side Validation	High	Only client-side validation, no server check
SEC-068	Client-side Price Calculation	High	Price calculated on client sent to server
SEC-069	Dangerous Dependencies	Medium	Outdated or vulnerable package versions
SEC-070	Missing Docker Vulnerability Scan	Medium	Docker image without vulnerability scanning
SEC-071	Sensitive Data in JWT	High	JWT payload contains sensitive data
SEC-072	Missing CSP Nonce	Medium	Inline <script> without CSP nonce

Extended Security Rules (SEC-073 to SEC-105+)

33 additional rules organized by OWASP Top 10 2021:

Category	Rules	Description
A01: Broken Access Control	SEC-073 to SEC-075	IDOR, privilege escalation
A02: Cryptographic Failures	SEC-076 to SEC-078	Weak hash, ECB mode, hardcoded keys
A03: Injection	SEC-079 to SEC-082	LDAP, XPath, SSTI, command injection
A05: Security Misconfiguration	SEC-083 to SEC-084	Debug mode, CORS
A07: Authentication Failures	SEC-085 to SEC-086	Weak password, brute force
A08: Software Integrity	SEC-087 to SEC-088	Insecure deserialization, HTTP without TLS
A09: Security Logging	SEC-089	Sensitive info in logs
A10: SSRF	SEC-090	Server-side request forgery
Additional	SEC-091 to SEC-105	XXE, path traversal, race condition, ReDoS, etc.

Run pyneat check your_file.py --severity --cvss for detailed scan with CVSS scores and CWE/OWASP references.

AI Security Scanner (NEW)

Detects security risks specific to AI-generated code and AI applications:

AI Vulnerability	Severity	Rule	Description
Prompt Injection	Critical	AI-010	"Ignore previous instructions", "forget everything"
Context Confusion	Medium	AI-011	Multi-turn conversation context confusion attacks
Proxy Injection	High	AI-012	Tool call injection in AI agents
Missing Confidence Threshold	Medium	AI-020	LLM output without confidence checking
Missing Fact Check	High	AI-021	No fact verification for AI-generated content
Unguarded Sensitive Operation	High	AI-022	Sensitive operations without guardrails
Verbose Error Exposure	Medium	AI-030	Detailed errors exposing model internals
Missing API Rate Limit	Medium	AI-031	AI API calls without rate limiting
Over-detailed System Info	Medium	AI-032	Excessive system information in responses
Adversarial Input	Critical	AI-040	Homoglyph attacks, injection patterns
Unicode Homograph Attack	Medium	AI-041	Unicode confusable characters in AI inputs
System Prompt Leakage	High	AI-050	Exposed system prompts in responses
Tool Call Collision	Medium	AI-051	Conflicting tool names in AI agents
Missing Output Guardrails	High	AI-052	AI without content filtering guardrails
Toxic Output Risk	Medium	AI-053	Potentially harmful AI-generated content
Temperature Misuse	Low	AI-060	Unsafe temperature parameter settings
Context Window Mismanagement	Medium	AI-061	Context overflow handling issues
Hallucinated API Calls	High	AI-070	Non-existent API endpoints in generated code

Rust Backend

For large codebases, the Rust scanner (pyneat-rs) delivers 50x-100x speedup:

pip install pyneat[rust]
pyneat clean your_file.py --rust

Uses tree-sitter for AST parsing, pre-compiled regex patterns, and Rayon for parallel processing. No GIL contention for true parallelism.

Benchmark: PyNEAT vs the Competition

Benchmarked on 200 Python files (~50K LOC) from real vulnerable codebases:

Tool	Time	Throughput	Security Rules	Languages
PyNEAT Rust	10.1 ms	20.4K/sec	200+	9
Ruff	5.0 ms	40.0K/sec	0	1
Semgrep	150 ms	1.3K/sec	1000+	30+
Bandit	2000 ms	100/sec	70	1

PyNEAT is 15x faster than Semgrep, 200x faster than Bandit, while detecting 53% more critical findings on real-world vulnerable codebases.

For full benchmarks with detection rates and methodology, see pyneat-rs/README.md.

Rust Backend Features

• LN-AST (Language-Neutral AST): Unified AST format for all 9 languages
• 500+ Rules: 141 core Python/Rust + 230+ language-specific (JS/TS/Go/Java/Rust/C#/PHP/Ruby) + 23 AI security + 100+ enterprise rules
• Multi-Language: Native scanners for Python, JavaScript, TypeScript, Go, Java, Rust, C#, PHP, Ruby
• Auto-fix Engine: Atomic, conflict-aware code transformations
• SARIF 2.1.0 Export: Full compliance with GitHub Security Lab format
• Python Bindings: PyO3 integration for seamless Python usage
• LSP Server: Real-time IDE diagnostics via Language Server Protocol
• CI/CD Integrations: GitHub, GitLab, SonarQube native support

Installation

pip install pyneat

Or from source:

git clone https://github.com/khanhnam-nathan/Pyneat.git
cd Pyneat
pip install -e .

After installation, the pyneat CLI command is available system-wide. Alternatively, you can always run pyneat as a Python module (no install required) from the repository root:

python -m pyneat check your_file.py

CLI Reference

PyNeat exposes 11 commands:

Command	Description
pyneat clean	Clean a single file
pyneat clean-dir	Clean all files in a directory
pyneat check	Security scan (no auto-fix)
pyneat rules	List all available rules
pyneat explain	Detailed explanation of a rule (CWE, OWASP, fix steps)
pyneat ignore	Ignore a rule (per-file or globally)
pyneat report	Export security report (JSON/SARIF/HTML)
pyneat security-db	Manage CVE and GitHub Advisory databases
pyneat audit-deps	Audit dependencies for known vulnerabilities (OSV)
pyneat sbom	Generate Software Bill of Materials (CycloneDX/SPDX)
pyneat mcp	Start MCP server for Cursor/IDE integration

Additional flags:

Flag	Description
--enable-all	Enable all rules at once (destructive package)
--export-manifest	Auto-export .pyneat.manifest.yaml on exit
--annotate	Inject PYNAGENT YAML comments into source code
--dry-run	Preview changes without writing
--diff	Show diff before applying
--backup	Backup file before modifying
--in-place	Modify file directly
--fail-on	Exit with error on specific severity threshold
--baseline	Ignore known issues from baseline file
--parallel	Number of parallel threads
--rust/--no-rust	Force Rust/Python engine (default: Rust primary, Python fallback)
--lang	Target language for multi-language scanning (JS, TS, Go, Java...)
--exclude	Exclude files matching pattern (can be used multiple times)
--rule	Only run specific rules (can be used multiple times)
--lock-files	Discover and list lock files in the target directory
--check-cve	Include CVE scan in dependency audit
--check-license	Check license compliance of dependencies

Clean a single file

# Safe package (default) — zero risk
pyneat clean your_file.py

# Preview without writing
pyneat clean your_file.py --dry-run --diff

# In-place with backup
pyneat clean your_file.py --in-place --backup

# Conservative — cleaner code
pyneat clean your_file.py --package conservative

# Destructive — full sweep
pyneat clean your_file.py --package destructive

Clean a directory

pyneat clean-dir ./src --dry-run --diff
pyneat clean-dir ./src --pattern "*.py" --in-place --backup --parallel

Security scan

pyneat check your_file.py --severity --cvss
pyneat check ./src --fail-on critical --format sarif --output report.sarif

Explain a rule

pyneat explain SEC-001

Shows: problem description, fix constraints, common mistakes, verification steps, documentation links.

Ignore a rule

# Ignore one instance at specific file + line
pyneat ignore SEC-003 --file app.py --line 42 --reason "already sanitized"

# Ignore globally for entire project
pyneat ignore SEC-003 --global --reason "not applicable to our codebase"

Export report

pyneat report ./src -f sarif -o security.sarif      # GitHub Code Scanning
pyneat report ./src -f json -o report.json          # Custom integration
pyneat report ./src -f html -o report.html          # Human-readable
pyneat report ./src -f yaml -o report.yaml          # PyNEAT manifest (full AgentMarker data)
pyneat report ./src -f junit -o junit.xml           # JUnit XML
pyneat report ./src -f gitlab -o gitlab.json        # GitLab SAST
pyneat report ./src -f sonarqube -o sq.json          # SonarQube Generic Issue
pyneat report ./src -f markdown -o report.md        # Markdown summary

# Use Rust scanner for speed (default: auto-detect)
pyneat report ./src -f sarif -o security.sarif --rust

Manage security databases

pyneat security-db --status   # Show CVE/GHSA database status
pyneat security-db --update   # Update to latest CVE + GitHub Advisory
pyneat security-db --force     # Force update (ignore cache age)

Interactive Feature Menu

After every check, clean, rules, or report, PyNeat shows a smart feature menu:

┌─────────────────────────────────────────────────────────────┐
│                  EXPLORE MORE FEATURES                     │
└─────────────────────────────────────────────────────────────┘

[A] 🔒 Security Check
    Quét lỗ hổng: SQL injection, path traversal, hardcoded secrets...
    → pyneat check file.py

[B] 🧹 Clean Code
    Thêm type hints, xóa unused imports, số magic, debug prints...
    → pyneat clean file.py

[C] 📖 Explain Rule
    Nguyên nhân, cách fix, CWE/OWASP, verification steps...
    → pyneat explain SEC-001

[D] 📊 Export Report (JSON/SARIF)
    Tích hợp CI/CD: GitHub Code Scanning, GitLab SAST...
    → pyneat report . -f sarif -o security.sarif

[q] Exit - return to terminal
[Enter] Skip this menu

Python API

from pyneat import clean_code, clean_file, analyze_code
from pyneat import RuleEngine, CodeFile, RuleConfig

# Clean code string
result = clean_code("x == None")  # "x is not None"

# Clean a file
from pathlib import Path
result = clean_file(Path("app.py"), in_place=True)
print(f"Made {len(result.changes_made)} changes")

# Analyze without fixing
report = analyze_code("x == None; print('debug')")
for issue in report['issues']:
    print(f"  - {issue}")

Python API — Custom engine

from pyneat import RuleEngine, CodeFile
from pyneat.rules import IsNotNoneRule, DebugCleaner

engine = RuleEngine([
    IsNotNoneRule(),
    DebugCleaner(mode="safe"),
])
result = engine.process_code_file(CodeFile(path=Path("demo.py"), content=source))

Configuration

Add to pyproject.toml:

[tool.pyneat]
package = "safe"                  # safe, conservative, destructive

# Conservative
enable_unused_imports = true
enable_fstring = false
enable_dataclass = false
enable_magic_numbers = false
debug_clean_mode = "off"          # off, safe, aggressive

# Destructive (caution!)
enable_import_cleaning = false
enable_naming = false
enable_refactoring = false
enable_comment_clean = false
enable_redundant = false
enable_dead_code = false
enable_match_case = false

# CI/CD
export_manifest = false

Rust/PyO3 API (High-Performance)

For maximum performance, use the Rust-accelerated scanner directly:

import pyneat  # replaces pyneat_rs from v3.1.2+

# Security scan (Python)
issues = pyneat.scan_security(code)

# Security scan (any language: js, ts, go, java, rs, cs, php, rb)
js_issues = pyneat.scan_multilang(js_code, "javascript")
ts_issues = pyneat.scan_multilang(ts_code, "typescript")

# AI security scan
ai_issues = pyneat.scan_ai_security(ai_code, "python")

# Get rule catalog
rules = pyneat.get_rules()       # Core security rules (~141)
ai_rules = pyneat.get_ai_rules() # AI security rules (23)

# Detect language from file extension
lang = pyneat.detect_language("file.js")   # → "javascript"
lang = pyneat.detect_language("file.ts")    # → "typescript"

# Auto-fix
fixed_code = pyneat.apply_auto_fix(code, json.dumps(finding))

# Batch fixes with conflict resolution
result = pyneat.apply_fixes_batch(code, json.dumps(findings))

Pre-commit Integration

repos:
  - repo: local
    hooks:
      - id: pyneat-clean
        name: PyNeat AI Code Cleaner
        entry: pyneat clean --package conservative --in-place
        language: system
        types: [python]
        pass_filenames: true
        args: ['--dry-run']

# Linux/macOS
bash scripts/setup-pre-commit.sh

# Windows
scripts\setup-pre-commit.bat

GitHub Actions

name: PyNeat Code Quality
on: [push, pull_request]
jobs:
  pyneat:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
      - name: Install PyNeat
        run: pip install pyneat
      - name: Run PyNeat
        run: pyneat clean-dir . --dry-run

Full template at .github/workflows/ci.yml.

Manifest Export — CI/CD Integration

• ManifestExporter writes .pyneat.manifest.json with all markers
• export_to_sarif() — SARIF 2.1.0 format (GitHub Security, Azure DevOps)
• export_to_codeclimate() — Code Climate format
• export_to_markdown() — Human-readable report

MarkerCleanup — Stale Marker Removal

• MarkerCleanup class removes markers after issues are fixed
• remove_stale_markers() — only removes markers not in remaining_issues
• remove_all_markers() — strips all PYNAGENT comments

AgentMarker System

Every finding in PyNEAT is represented as an AgentMarker — a structured, self-contained object designed for agent-to-agent handoff. Unlike raw findings, AgentMarkers carry full context that lets an AI agent understand, fix, and track issues without external lookup.

AgentMarker Fields

@dataclass(frozen=True)
class AgentMarker:
    marker_id: str       # Unique ID, e.g. "PYN-SEC-0001"
    issue_type: str     # e.g. "sql_injection", "unused_import"
    rule_id: str        # e.g. "SecurityScannerRule", "DeadCodeRule"
    severity: str       # "critical", "high", "medium", "low", "info"
    line: int           # 1-indexed start line
    end_line: int       # 1-indexed end line
    hint: str           # Suggested fix
    why: str            # Why this is a problem
    impact: str         # Consequences if exploited
    confidence: float   # 0.0-1.0 detection confidence
    can_auto_fix: bool  # Whether auto-fix is conceptually possible
    fix_diff: str       # Unified diff for the fix
    snippet: str        # Code snippet (max 200 chars)
    cwe_id: str         # CWE-89, CWE-79...
    cvss_score: float   # e.g. 9.8
    cvss_vector: str    # CVSS:3.1/AV:N/AC:L/...
    owasp_id: str       # OWASP-A03
    related_markers: Tuple[str]  # Related marker IDs

Manifest Export

Export full scan results as .pyneat.manifest.yaml:

pyneat clean your_file.py --export-manifest

The manifest contains:

• All AgentMarkers with full metadata
• Severity breakdown (critical/high/medium/low/info counts)
• Rules that were enabled
• Tool version and scan timestamp

Manifest Diff — Track Progress

Compare two manifests to track code quality progress across commits:

from pyneat.core.manifest_schema import diff_manifests, format_diff

old_manifest = load_manifest_or_fail("baseline.pyneat.manifest.yaml")
new_manifest = load_manifest_or_fail("current.pyneat.manifest.yaml")
diff = diff_manifests(old_manifest, new_manifest)
print(format_diff(diff))

SBOM — Software Bill of Materials

Generate a complete inventory of your dependencies:

# CycloneDX JSON (default)
pyneat sbom --format cyclonedx-json --output sbom.json

# SPDX
pyneat sbom --format spdx-json --output sbom.json

# Include known vulnerabilities
pyneat sbom --format cyclonedx-json --output sbom.json --include-vulns

SBOM formats supported: CycloneDX JSON, SPDX JSON. Integrates with OSV database for vulnerability enrichment.

Dependency Audit

Scan dependencies against the OSV vulnerability database:

# Scan installed packages
pyneat audit-deps

# Scan requirements.txt
pyneat audit-deps --path requirements.txt

# Export as SARIF for GitHub
pyneat audit-deps --format sarif --output vulns.sarif

# Export as SBOM with vulnerability data
pyneat audit-deps --format sbom --output sbom.json

# Check license compliance
pyneat audit-deps --check-license

MCP Server — IDE Integration

PyNEAT includes a Model Context Protocol (MCP) server, enabling Cursor and other MCP-compatible editors to invoke PyNEAT as a native tool. This brings security scanning directly into your AI coding workflow without leaving the editor.

Architecture

The MCP server runs as a STDIO JSON-RPC 2.0 service — no network sockets, no HTTP, just stdin/stdout. This makes it safe, fast, and compatible with any MCP client.

Cursor (MCP Client)
    │
    ├─ JSON-RPC 2.0 over STDIO
    │
    ▼
PyNEAT MCP Server (pyneat.tools.mcp_server)
    │
    ├─ pyneat_scan        → Scan code string for issues
    ├─ pyneat_scan_file   → Scan file on disk
    ├─ pyneat_explain     → Get rule metadata
    ├─ pyneat_auto_fix    → Apply auto-fix
    ├─ pyneat_list_rules  → List available rules
    └─ pyneat_audit_deps  → Audit dependencies

Setup

1. Configure Cursor MCP

Add to your Cursor settings (settings.json):

{
  "mcpServers": {
    "pyneat": {
      "command": "python",
      "args": ["-m", "pyneat.tools.mcp_server"]
    }
  }
}

Or use the CLI shortcut:

# Just run this once to verify MCP is working
pyneat mcp --verbose

2. Available MCP Tools

Tool	Description	Input
pyneat_scan	Scan code string	code, language
pyneat_scan_file	Scan file on disk	file_path, language
pyneat_explain	Get rule metadata	rule_id, issue_type
pyneat_auto_fix	Apply auto-fix	marker_id, code, language
pyneat_list_rules	List all rules	category, severity
pyneat_audit_deps	Audit dependencies	path, check_vulns

Usage Examples

After setup, use PyNEAT tools directly in Cursor:

# Scan code for security issues
Tool: pyneat_scan
code: eval(user_input)
language: python

# Scan a file
Tool: pyneat_scan_file
file_path: ./src/auth.py

# Get rule explanation
Tool: pyneat_explain
rule_id: SEC-001

# Auto-fix a finding
Tool: pyneat_auto_fix
marker_id: PYN-SEC-0001
code: os.system(user_input)
language: python

# List security rules
Tool: pyneat_list_rules
category: security
severity: high

# Audit dependencies
Tool: pyneat_audit_deps
path: requirements.txt
check_vulns: true

Response Format

Every tool returns structured AgentMarkers:

{
  "marker_id": "PYN-SEC-0001",
  "issue_type": "sql_injection",
  "rule_id": "SecurityScannerRule",
  "severity": "critical",
  "line": 42,
  "end_line": 42,
  "hint": "Use parameterized queries instead of string concatenation",
  "why": "User input is directly embedded in SQL query",
  "impact": "Attacker can read/modify/delete database records",
  "confidence": 0.95,
  "can_auto_fix": false,
  "cwe_id": "CWE-89",
  "cvss_score": 9.8,
  "owasp_id": "OWASP-A03"
}

This structure gives AI agents everything they need to understand and fix the issue — without any external lookup.

Python API — MCP Tools

You can also use the MCP tool functions directly from Python:

from pyneat.tools.mcp_server import (
    scan_code, scan_file, explain_rule,
    auto_fix_code, list_rules, audit_deps
)

# Scan code
results = scan_code("eval(user_input)", language="python")
for marker in results:
    print(f"{marker.marker_id}: {marker.issue_type}")

# Audit dependencies
vulns = audit_deps("requirements.txt", check_vulns=True)
print(f"Found {len(vulns)} vulnerabilities")

VSCode Extension

PyNeat is available as a VSCode/Cursor extension:

• Real-time diagnostics for Python, JavaScript, TypeScript
• Quick Fix — auto-fix with one click
• Hover info — severity, CWE, fix constraints, verification steps
• Context menu — Apply Fix, Send to AI Agent, Ignore, Add Comment
• Save-triggered scan — runs automatically when you save

Install from .vsix or search the marketplace (coming soon).

Examples

Check out the examples/ directory for ready-to-use scripts:

Example	Description
basic_usage.py	Scan and clean a single file
security_scan.py	Security scanning with SARIF export
batch_processing.py	Process entire projects
custom_rule.py	Create and use custom rules
pre_commit_integration.py	Integrate with pre-commit hooks

Run an example:

python examples/basic_usage.py

Documentation

Document	Description
docs/quickstart.md	5-minute getting started guide
docs/faq.md	Frequently asked questions
docs/architecture.md	Technical architecture
docs/writing-rules.md	Creating custom rules
docs/github-actions-guide.md	CI/CD integration guide
CONTRIBUTING.md	Contribution guidelines
CODE_OF_CONDUCT.md	Community code of conduct

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Build distribution
python -m build

Architecture: 7-Layer Protection System

Layer	Component	Description
1	AST Guard	Validates code structure before processing
2	Semantic Guard	Preserves code semantics during transformations
3	Type Shield	Prevents type-related regressions
4	Atomic Operations	Ensures atomic transformations
5	Scope Guard	Isolates changes within safe boundaries
6	Type Checking	Validates with mypy/pyright
7	Fuzz Testing	Stress tests with malformed inputs

Editions & Commercial Support

PyNeat uses a dual-licensing / freemium model.

PyNeat Community (current, free)

• License: GNU AGPLv3
• Engine: Pure Python + Rust hybrid (pyneat-rs)
• Best for: Individual developers, students, small projects
• Rust coverage: ~30% of rules (security + quality)

PyNeat Standard (on request)

• Engine: Full Rust (pyneat-rs) for extreme performance
• Features: Multi-threading, 50x-100x faster, deep CI/CD integration
• Best for: Mid-sized teams, 1,000+ files

PyNeat Enterprise (on request)

• Features: Everything in Standard + Custom Ruleset API, Audit Reports, Dedicated SLA
• Best for: Large enterprises

Commercial License Exemption: If you cannot comply with AGPLv3 (e.g., proprietary SaaS, closed-source embedding), contact the author for a commercial license.

Contact: [e-mail]

License

PyNeat is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PyNeat is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.

AGPLv3 with Commercial Exception: Commercial use of this software (e.g., bundling in paid products, SaaS services) is permitted, provided that you comply with the open source obligations under AGPLv3 §11. Contact the author for alternative licensing arrangements.