Results for “patterns”

Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns.

Query AWS CloudTrail events with boto3, build statistical baselines of normal API activity, and detect anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns.

Extract and catalog attack patterns from cyber threat intelligence reports into a structured STIX-based library mapped to MITRE ATT&CK for detection engineering and threat-informed defense.

Turns any long-running domain (business, relationships, health, career, learning, investing) into a local knowledge project with four layers, source tags, immutable snapshots, and a concept library that learns patterns over time.

Reverse-engineers custom JavaScript-based WASM virtual machines and risk-control engines by identifying DSL VM patterns, extracting opcodes, analyzing constant tables, and tracing exported functions through static analysis and runtime injection.

Audits codebases against PCI DSS v4.0, scanning for PAN patterns, CVV storage, and all 12 requirements including network security, encryption, access control, and logging. Estimates SAQ type for payment-handling systems.

Write and refine user stories using INVEST criteria, the 'As a X, I want Y, so that Z' format, Gherkin acceptance criteria, definition of ready, anti-patterns, and a review checklist.

Builds detailed voice profiles for characters, narrators, brands, or AI agents, covering diction, syntax, rhythm, worldview, signature patterns, and failure modes, and can reverse-engineer profiles from writing samples or audit existing ones.

Detect and exploit Server-Side Template Injection vulnerabilities across multiple template engines including Jinja2, Twig, Freemarker, and Velocity, with payloads for sandbox escape and remote code execution. Includes detection methodology, bypass techniques, and fix patterns.

This skill should be used when the user asks about "parallel agents", "background tasks", "run_in_background", "non-blocking agents", "check agent progress", "TaskOutput", "retrieve agent results", or discusses running multiple agents concurrently. Covers patterns for launching agents in background, monitoring progress, and retrieving results.

Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.

Modernize deprecated MATLAB functions and patterns. Use when check_matlab_code or checkcode reports "not recommended" or "to be removed" warnings, when migrating legacy code, or when replacing deprecated APIs (trainNetwork, csvread, xlsread, datenum, eval, subplot, guide, optimset, wavread, svmtrain, uicontrol) with current equivalents.

Review this PR or diff for quality, security, and maintainability. Use when reviewing pull requests, examining a named change set, or the user asks for a code review of current work. Repo-wide anti-patterns → audit-code-quality. Bulk transform semantics → audit-codemod-safety.

CRUD patterns for state slices — consistent `domain/create|read|update|delete` action naming, request/success/failure triples per operation, and normalised `{ byId, allIds }` entity shapes for O(1) lookups. Use when designing a new entity slice, standardising action-type names, or refactoring array-shaped state.

OpenStack Heat orchestration service skill. Use when working with HOT templates, stack lifecycle management, auto-scaling groups, nested stacks, resource type registry, template validation, or infrastructure-as-code patterns within OpenStack. Covers deployment via Kolla-Ansible, template authoring, stack operations, and troubleshooting common orchestration failures.

VS-Enhanced Academic Style Humanizer - Transforms writing patterns to achieve authentic scholarly voice Applies transformations from G5 analysis to create natural academic prose Use when: improving AI-assisted writing quality, preparing manuscripts, enhancing scholarly voice Triggers: humanize, transform, make natural, improve writing quality, improve style

Provides file paths to language-specific reference files for polyglot test analysis skills, enabling framework-aware detection of test markers, assertions, and patterns across .NET, Python, TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++.

Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working, helping inspire and improve your own ad campaigns.

Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

Hunt for adversary abuse of Living Off the Land Binaries (LOLBins) by analyzing endpoint process creation logs for suspicious execution patterns of legitimate Windows system binaries used for malicious purposes.

Analyzes and recreates Uber's design language: a black-and-white palette, custom geometric display and text fonts, pill-shaped interactive elements, and editorial 4:3 illustrations for transportation and delivery interfaces.

Documents, standardizes, and governs a Vue 3 + shadcn-vue design system, including component documentation templates, patterns for forms, empty states, loading states, and feedback, plus a consistency checklist and conventional commit conventions.

Python code-quality anti-patterns and review checks: exception-hierarchy correctness, singleton identity comparison, narrow exception handling, wildcard-import avoidance, magic-number naming, and dead-local removal. Use when reviewing or self-reviewing Python code for correctness and readability defects that linters and reviewers should catch.

This skill should be used when building React components with TypeScript, typing hooks, handling events, or when React TypeScript, React 19, Server Components are mentioned. Covers type-safe patterns for React 18-19 including generic components, proper event typing, and routing integration (TanStack Router, React Router).

Implement signal-based reactive state management in Angular v20+. Use for creating reactive state with signal(), derived state with computed(), dependent state with linkedSignal(), and side effects with effect(). Triggers on state management questions, converting from BehaviorSubject/Observable patterns to signals, or implementing reactive data flows.

Dart 3.x language feature standards: null safety, records, sealed classes, switch pattern matching, extensions, and async/await. Use when using !, ?., ??, late, sealed classes, record types, switch expressions, or async patterns — and before introducing any new Dart 3.x construct to confirm the modern idiomatic approach.

Implements any user intent, requirement, story, bug fix or change request by producing clean working code artifacts that follow the project's existing architecture, patterns and conventions. Use when the user wants to build, fix, tweak, refactor, add or modify any code, component or feature.

Design and implement accessible UI motion systems for React, Next.js, and frontend interfaces. Use for animation, transitions, gestures, drag and drop, page transitions, staggered reveals, SVG motion, loaders, motion tokens, reduced-motion behavior, or the old motion-foundations/motion-patterns/motion-advanced/motion-ui skills.

USB and CAN connectivity for Zephyr RTOS. Covers USB device stack configuration (CDC ACM, HID, MSC), CAN controller integration, and professional USB-to-CAN adapter patterns including buffering and protocol packetization. Trigger when adding USB interfaces, implementing CAN bus communication, or building hardware diagnostic tools.

Read BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link simulations.

Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins using statistical analysis on Splunk or raw log data.

Guides OLTP database schema design through six stages: domain and access patterns, entities and relationships, keys and constraints, normalization trade-offs, indexing and performance, and operations and evolution. Includes a final review checklist and tips for effective guidance.

Guides the agent to research existing tools, libraries, and patterns before writing custom code, using parallel searches across package registries, MCP servers, skills, and GitHub, then evaluating and deciding to adopt, extend, or build.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Manage project learnings. Review, search, prune, and export what gstack has learned across sessions. Use when asked to "what have we learned", "show learnings", "prune stale learnings", or "export learnings". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"