lang

Comprehensive Bash shell scripting and automation guide covering fundamentals, control flow, I/O pipelines, process control, system administration automation, testing/debugging, security, design patterns, and penetration testing automation (Nmap, web scanning, exploitation, Wi-Fi assessment, reporting). MUST load when .sh files are detected or shell scripts are being written. For Docker-specific patterns, use cloud:practicing-devops instead. For broader DevOps methodology, use cloud:practicing-devops instead.

Comprehensive database development guide covering relational design (ER modeling, normalization, PostgreSQL implementation), relation orthogonality and temporal data design (cross-relation duplication, 6NF verification, historical relation splitting), SQL antipatterns (25 patterns), database refactoring (schema migration, refactoring catalog), database internals (B-tree/LSM, distributed systems, consensus algorithms), PostgreSQL operations (config, extensions, query tuning, MVCC/VACUUM, backup/PITR, replication/HA, monitoring), and analytical batch pipeline operations (job flow, incremental/full-refresh updates, SQL job testing). Use when designing database schemas, eliminating cross-table duplication, designing time-versioned tables, planning schema refactoring, writing SQL queries, choosing storage engines, tuning PostgreSQL performance, managing backup/replication, or designing batch data pipelines. Replaces: designing-relational-databases, avoiding-sql-antipatterns, understanding-database-internals.

Comprehensive Go development guide covering clean code practices, design patterns (GoF/concurrency/DDD/CQRS), production patterns (net/http server & client with chi, database/sql with sqlc/GORM, JSON/CSV/Excel formats, html/text templates, slog/zap/zerolog logging, graceful shutdown, distroless containers), and internals (type system/memory layout/reflection/G-M-P scheduler). MUST load when go.mod is detected or Go code is being written. Covers naming, error handling, goroutine/channel concurrency, table-driven testing, nil handling, project structure, function design, data structures, Functional Options, refactoring strategies, and performance optimization. For application logging design and structured logging patterns, use cloud:implementing-observability. For language-agnostic clean code/SOLID and refactoring, use devkit:writing-clean-code. For Clean Architecture/DDD strategic design, use devkit:applying-clean-architecture. For algorithm/data-structure complexity analysis, use solving-algorithms.

Comprehensive MCP (Model Context Protocol) development guide covering architecture (Host/Client/Server roles, Tools/Resources/Prompts, Control Segregation), server and client implementation with TypeScript SDK (including MCP Gateway pattern and MCP Inspector debugging), protocol specification (JSON-RPC 2.0, stdio/Streamable HTTP transports, session management), security threats and countermeasures (Prompt Injection, Tool Poisoning, Shadowing, Rug Pull, OAuth 2.1 authorization, container sandboxing), and the MCP ecosystem (registries, official SDKs, A2A protocol relationship). MUST load when building MCP servers or clients. For Claude Code plugin MCP configuration, use devkit:authoring-plugins instead. For MCP integration with Vercel AI SDK in web apps, see ai:integrating-ai-web-apps. For consuming MCP tools from LangGraph agents, see ai:building-ai-agents.

Modern Python development guide covering project setup, tooling, Pythonic best practices, OOP design principles, GoF design patterns, SE process, and type safety. MUST load when pyproject.toml or requirements.txt is detected. Covers Python 3.13 + uv + ruff + mypy, FastAPI/FastMCP, pytest, Docker, Pythonic best-practice idioms, software design patterns (encapsulation, LSP, Design by Contract), SE practices (SDLC, Scrum/Kanban, CI/CD, deployment), type safety rules (Any prohibition, TypedDict, Protocol, dataclass), Architecture Patterns (Repository, Unit of Work, Aggregates, Domain Events, CQRS, Event-Driven Architecture), and DDD Tactical Patterns (Value Object, Entity, Aggregate Root). For language-agnostic clean code principles, use devkit:writing-clean-code. For application logging design, use cloud:implementing-observability. For Clean Architecture in Python, see references/CA-PYTHON.md. For general Clean Architecture principles, use devkit:applying-clean-architecture.

Comprehensive R development guide covering project setup, R scripts, packages, data structures, vectorization, tidyverse data wrangling, ggplot2 visualization, statistics, simulation, tidymodels machine learning, AI API workflows, Shiny dashboards, Quarto/R Markdown reporting, renv reproducibility, testthat testing, debugging, performance, parallel R, and C/C++/Python interop. MUST load when .R, .Rmd, .qmd, renv.lock, DESCRIPTION, NAMESPACE, .Rproj, app.R, RStudio, tidyverse, dplyr, ggplot2, Shiny, Quarto, R package development, or R data analysis is involved.

Comprehensive algorithm and data structure reference for competitive programming covering sorting, searching, trees, graphs, dynamic programming, computational geometry, and number theory with complexity analysis and language-agnostic implementations. Use when solving algorithmic problems, optimizing code for time/space complexity, or implementing classic data structures (stack, queue, heap, BST, union-find). For database-specific data structures (B-tree, LSM), use developing-databases instead.

Practical techniques for writing correct, effective, and fast SQL queries across engines: set-based declarative thinking, CASE expressions, conditional aggregation, descriptive statistics, and analysis patterns, GROUP BY/HAVING and characteristic functions, window functions (frames, inter-row comparison, correlated-subquery replacement, sessionization), joins and subqueries (self-join, cross join, outer-join pivot, tally/hierarchical), set operations and quantification (EXISTS, universal/existential), NULL and three-valued logic, and execution-plan-based query rewriting (SARGable predicates, slow-to-fast rewrite catalog, cross-DBMS dialect portability). Use when writing, reviewing, or optimizing SQL SELECT queries, choosing joins vs subqueries, applying window functions, or rewriting slow queries. For schema design, normalization, SQL antipatterns, DB internals, and PostgreSQL operations, use lang:developing-databases instead. For BigQuery/GCP analytics SQL, use cloud:developing-google-cloud instead.