security & testing Claude 스킬을 발견하세요. 105개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Generates comprehensive test case documentation and Testany-compatible automation scripts from natural language requirements.
Provides core concepts, entity relationships, and configuration references for the Testany automation and testing platform.
Automates the UI testing workflow by using screenshots and Claude Vision to identify layout issues and visual bugs without writing test code.
Automates visual quality assurance by capturing and analyzing screenshots across web and mobile platforms using Claude's vision capabilities.
Automates test suite generation and increases code coverage using AI-driven semantic analysis and edge-case detection.
Implements von Holst’s reafference theory to distinguish self-generated AI interactions from external anomalies and security threats.
Validates and automates Riehl-Shulman covariant fibrations for directed type theory within synthetic infinity-categories.
Hardens operational security and minimizes surveillance exposure for privacy-critical applications and communications infrastructure.
Automates the creation and improvement of test suites using AI-driven tools and coverage gap analysis.
Automates and optimizes modern end-to-end testing workflows using the Playwright framework for robust web application verification.
Conducts deep code investigations to identify and resolve the underlying source of bugs rather than patching superficial symptoms.
Automates end-to-end testing with AI-powered self-healing capabilities to maintain robust UI test suites.
Diagnoses and resolves software bugs, errors, and failing tests using the Codex CLI for autonomous code repair and verification.
Verifies code, documentation, and implementations through multi-model consensus to ensure accuracy and compliance with requirements.
Automatically detects project tech stacks and executes a comprehensive parallel security analysis using relevant scanners and AI subagents.
Verifies the global consistency of modular code structures and data schemas using mathematical Čech cohomology principles.
Provides rigorous correctness guarantees for AI systems through automated theorem proving, interval arithmetic, and categorical proofs.
Verifies global consistency across modular codebases using topological sheaf cohomology and Čech descent conditions.
Optimizes shell script reliability using advanced 2025 debugging patterns, error recovery, and performance profiling techniques.
Streamlines the creation of comprehensive Python testing suites using pytest, advanced mocking, and async patterns.
Provides comprehensive security guidelines, threat mitigation strategies, and hardened configuration patterns for Docker environments.
Transforms mathematical proofs into interactive games using Gödel's Dialectica interpretation for formal verification and strategy extraction.
Facilitates higher-dimensional type theory and formal verification using observational bridge types and structured proof assistants.
Implements robust Nostr event signing patterns and abstractions for browser extensions, remote signing, and custom implementations.
Implements a structured, evidence-based workflow to identify, isolate, and resolve code errors and system failures effectively.
Implements advanced cryptographic primitives and mechanism design patterns to enable protocol-aligned value extraction and MEV mitigation.
Automates the creation of comprehensive test suites by analyzing code paths and edge cases to ensure maximum coverage.
Implements systematic security frameworks like STRIDE and DREAD to identify, analyze, and mitigate threats during system design.
Implements and enforces production-grade security standards, RBAC, and network micro-segmentation for Kubernetes clusters.
Standardizes the process of analyzing, reviewing, and enhancing code maintainability and performance through systematic validation.
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