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$22

Convert UI screenshots into production-ready code with accurate styling and structure.

Static analysis security scanner that identifies OWASP vulnerabilities across backend, frontend, mobile, and library code. \n \n Scans for SQL injection, XSS, BOLA, BFLA, SSRF, prototype pollution, unsafe deserialization, ReDoS, path traversal, and zip slip vulnerabilities \n Three scan depths available: quick (default), balanced, and full, with token usage warnings for comprehensive scans \n Automated workflow: plans vulnerability vectors per project type, nominates candidate files, analyzes fi

Comprehensive code review for diffs with auto-detected domain-specific checks. \n \n Analyzes changed code for security vulnerabilities, anti-patterns, quality issues, and breaking changes across generic, frontend, and backend domains \n Auto-detects domain (frontend, backend, CLI, config) from file paths and applies relevant checklists \n Generates structured markdown reports with severity levels, file locations, code snippets, and actionable recommendations \n Performs impact analysis to ident

Systematic code maturity evaluation across 9 security and engineering categories with evidence-based ratings. \n \n Assesses arithmetic safety, auditing, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing using Trail of Bits' framework \n Produces a maturity scorecard with 0–4 ratings per category, detailed findings with file references, and a priority-ordered improvement roadmap \n Combines automated code analysis with interactive clarification

Modern Python tooling, naming conventions, type checking, and documentation standards for maintainable codebases. \n \n Configure ruff for unified linting and formatting, replacing flake8, isort, and black with a single fast tool \n Set up strict type checking with mypy or pyright to catch errors before runtime \n Follow PEP 8 naming conventions: snake_case for functions/variables, PascalCase for classes, SCREAMING_SNAKE_CASE for constants \n Write Google-style docstrings for all public APIs wit

Technology-agnostic prompt generator for identifying and documenting high-quality code exemplars across multiple languages. \n \n Supports seven programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with auto-detection capability \n Configurable analysis depth (Basic, Standard, Comprehensive), categorization method (Pattern Type, Architecture Layer, File Type), and documentation format \n Generates exemplars.md files with real file references, descriptions, and optio

AI-powered code review using CodeRabbit, triggered on explicit request or autonomously when quality/security issues are detected. \n \n Identifies bugs, security vulnerabilities, and quality risks; groups findings by severity (Critical, Warning, Info) \n Supports reviewing staged, committed, or all changes; can compare against specific branches or commit hashes \n Offers two output modes: --plain for detailed feedback with fix suggestions, or --prompt-only for minimal agent-optimized output \n E

Comprehensive guide to code generation techniques including template engines, AST manipulation, code scaffolding, and automated boilerplate generation for increased productivity and consistency.

Generates and validates technical documentation across docstrings, API specs, and developer guides. \n \n Supports multiple docstring formats (Google, NumPy, Sphinx for Python; JSDoc for TypeScript) and API specification standards (OpenAPI, AsyncAPI, gRPC) \n Includes validation workflows for each format: doctest/pytest for Python, TypeScript compilation checks, and Redocly linting for OpenAPI specs \n Covers inline code documentation, interactive API portals, documentation site generation, and