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Found 3,046 Skills
Skill for Playwright-driven web UI testing, exploration, test generation, execution, and debugging (TypeScript or Python). Triggers for tasks involving application testing, validation, test generation, or UI automation in web applications.
REST Assured Java library for testing REST APIs. Covers HTTP requests, JSON/XML validation, and Spring Boot integration. USE WHEN: user mentions "rest assured", "api test java", "rest api test", asks about "given when then", "RestAssured", "HTTP test", "JSON validation" DO NOT USE FOR: Unit tests - use `junit`; E2E browser tests - use Selenium; WebSocket testing - use dedicated tools; Non-Java projects - use language-specific HTTP clients
Use this skill for JavaScript apps needing Excel-like UI using the Syncfusion Spreadsheet Component. Trigger for creating, viewing, editing Excel (.xlsx, .xls, .xlsb) and CSV files; embedding spreadsheet editors; data binding from APIs/JSON; using formulas, charts, validation, filtering, or conditional formatting. Also trigger when users reference spreadsheet files ("open xlsx", "load Excel file", "add Syncfusion spreadsheet", "bind data to spreadsheet"). Do NOT trigger for standalone file processing without UI components.
Comprehensive guide for implementing Syncfusion WPF MaskedTextBox (SfMaskedEdit) control for restricted input with mask patterns. Use this skill when implementing masked input, input masks, or input restrictions with predefined patterns in WPF. Covers phone number input, email validation, credit card input, formatted input with prompt characters, RegEx masks, and custom patterns for dates, currency, product keys, and zip codes.
Comprehensive guide for implementing the Syncfusion Angular DateRangePicker component. Use this when working with date range selection, preset ranges, range validation, or date constraints in Angular applications. Covers DateRangePicker API, events, formatting, and accessibility patterns.
Review Express.js security audit patterns for middleware and routes. Use for auditing Helmet.js, CORS, body-parser limits, and auth middleware. Use proactively when reviewing Express.js apps. Examples: - user: "Secure my Express app" → add Helmet.js and disable x-powered-by - user: "Check Express CORS config" → verify origin allowlists and credentials - user: "Review Express auth middleware" → check route order and coverage - user: "Scan for Express path traversal" → verify path normalization and validation - user: "Audit Express session config" → check secure, httpOnly, and sameSite flags
QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear
Enforces complete execution, mode-aware delivery, compact sub-agent communication, independent agent-review gating, validation, and reporting for implementation, bugfix, hardening, documentation, specification, architecture, design, review, and post-mortem tasks. Use whenever work must be completed, reviewed, validated, or documented through an explicit execution mode instead of handled ad hoc.
Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
Evaluates ML models for performance, fairness, and reliability. Use for metric selection, cross-validation strategies, overfitting/underfitting diagnosis, hyperparameter tuning, LLM evaluation, A/B testing, and production monitoring for model drift.