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Found 13,843 Skills
Identifies subdomains and suggests bounded contexts in any codebase following DDD Strategic Design. Use when analyzing domain boundaries, identifying business subdomains, assessing domain cohesion, mapping bounded contexts, or when the user asks about DDD strategic design, domain analysis, or subdomain classification.
Manage tickets with tk CLI. Triggers on "create ticket", "list tickets", "what's next", "blocked", "close ticket", "ticket status", "work on next ticket/issue".
Provides comprehensive guidance for searching and retrieving Maven components from Maven Central Repository (https://repo1.maven.org/maven2/). This skill enables searching by groupId, artifactId, version, and other coordinates, retrieving component metadata (POM files, JARs, sources, Javadoc), querying version history, and analyzing dependencies. Use when the user needs to find, verify, or retrieve Maven dependencies, check component versions, analyze dependency trees, or work with Maven coordinates.
Comprehensive guide for creating Telegram Mini Apps with React using @tma.js/sdk-react. Covers SDK initialization, component mounting, signals, theming, back button handling, viewport management, init data, deep linking, and environment mocking for development. Use when building or debugging Telegram Mini Apps with React.
Continuous integration and deployment pipelines, automated testing, build automation, and team workflows for game development.
Analyze how competitors would rank in AI search results for a given topic or query. Triggers on "analyze competition", "competitor analysis", "what ranks for", "who would rank", "competitive landscape".
Professional sub-skill for Matplotlib focused on high-performance animations, complex multi-figure layouts (GridSpec), interactive widgets, and publication-ready typography (LaTeX/PGF).
Systematic 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.
Test-Driven Development with Iron Laws enforcement. Use when writing any production code to ensure tests are written first. Includes testing-expert capabilities.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
Execute the same task repeatedly with clean context via handoff. Triggers on: recursive loop, repeat until, keep doing until, loop until done. REQUIRES a finish condition to stop.