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Framework & SEO Skills

Skills for specific frameworks and search engine optimization.

reflex-python​

Reflex Python web framework for building full-stack apps in pure Python. Components, state management, and deployment. Use when building Reflex apps, designing component hierarchies, or managing app state.

Triggers: When building web apps with Reflex, designing components, managing state, routing, or creating full-stack Python web applications. Tools: Bash(reflex:*) Bash(python:*) Read Write References: component-reference.md

Key capabilities:

  • App structure: initialization, entry points, page decorators, file-based routing, configuration
  • Component system: layout (box, flex, grid), display (text, heading, image), input (input, select, checkbox), feedback (alert, toast, spinner)
  • State management with rx.State classes, typed vars, event handlers, computed vars, and substates
  • Event handling: on_click, on_change, two-way binding, background tasks, event chaining
  • Styling with Radix UI design tokens, responsive props, light/dark themes
  • Routing with dynamic segments, programmatic navigation, on_load events, and 404 handling
  • Database integration via built-in SQLModel with automatic migrations
  • Deployment to Reflex Cloud or self-hosted via Docker

??? example "Example usage" Build a todo app: Defines a TodoState with a list of todos and input field, creates event handlers for add/delete/toggle, builds UI with rx.input, rx.button, and rx.foreach(TodoState.todos, render_todo) to render the list dynamically.

fastapi-patterns​

FastAPI patterns including dependency injection, Pydantic models, async endpoints, middleware, and testing. Use when building FastAPI applications, designing API endpoints, or reviewing FastAPI code.

Triggers: When building APIs with FastAPI, designing endpoints, implementing dependency injection, authentication, or testing FastAPI applications. Tools: Bash(python:*) Bash(uvicorn:*) Read Write References: endpoint-patterns.md

Key capabilities:

  • Route definitions with HTTP method decorators, path/query parameters, and APIRouter modules
  • Pydantic models for request/response: separate Create/Update/Response schemas, validation with Field()
  • Dependency injection with Depends(), chained dependencies, Annotated for reusable deps, yield-based cleanup
  • Async endpoint patterns: when to use async def vs def, pairing with async libraries
  • Middleware: CORS, custom timing, trusted hosts, GZip compression
  • Authentication: OAuth2 password flow, JWT decoding, API key headers, scopes
  • Background tasks, WebSocket support, and structured error handling
  • Testing with TestClient, dependency overrides, async tests, and WebSocket testing

??? example "Example usage" REST API for a blog: Creates Pydantic schemas for PostCreate, PostResponse, CommentCreate, defines APIRouter modules for /posts and /posts/{id}/comments, implements CRUD handlers with SQLAlchemy dependency injection, and adds pagination to list endpoints.

pandas-polars​

DataFrame operations with pandas and polars including groupby, joins, reshaping, and performance optimization. Use when manipulating tabular data, choosing between pandas and polars, or optimizing DataFrame code.

Triggers: When working with tabular data, performing DataFrame operations, data transformations, cleaning data, aggregating by group, or choosing between pandas and polars. Tools: Bash(python:*) Read Write References: api-comparison.md

Key capabilities:

  • Choosing between pandas (mature ecosystem, exploratory work, <1GB) and polars (faster, lower memory, lazy evaluation, 1GB+)
  • DataFrame I/O: Parquet over CSV, dtype enforcement, chunked reading, column selection
  • Selection and filtering with expressions (polars) and .loc[]/.iloc[] (pandas)
  • GroupBy and aggregation with named columns, window functions (over() in polars)
  • Joins and merges with explicit join types, duplicate checking, anti-joins
  • Reshaping with pivot and melt/unpivot for wide and long formats
  • Missing data handling: null counts, fill strategies, interpolation
  • String and datetime operations across both libraries
  • Performance optimization: lazy evaluation, avoiding apply(), categorical dtypes

??? example "Example usage" Process a large CSV with group statistics: Uses polars lazy mode to scan the CSV, applies filters before collection, groups by the requested column with multiple aggregations in one .agg() call, sorts results, and writes output to Parquet for downstream use.

flutter-development​

Flutter/Dart development including widget architecture, state management, navigation, and cross-platform patterns. Use when building Flutter apps, choosing state management, or designing responsive mobile layouts.

Triggers: When building mobile or cross-platform apps with Flutter, designing widgets, managing state, adding navigation, or creating responsive layouts. Tools: Bash(flutter:*) Bash(dart:*) Read Write References: widget-catalog.md

Key capabilities:

  • Widget architecture: StatelessWidget vs StatefulWidget, composition over inheritance, const constructors
  • Layout system: Row, Column, Expanded, Flexible, Stack, ListView.builder, responsive design with LayoutBuilder
  • State management options: setState (local), Provider (lightweight DI), Riverpod (type-safe), BLoC (event-driven)
  • Navigation with GoRouter: declarative routes, nested navigation with ShellRoute, redirect guards, deep linking
  • Theming with Material 3, ColorScheme.fromSeed, dark mode support, custom TextTheme
  • Networking with http/dio, json_serializable, FutureBuilder/StreamBuilder, repository pattern
  • Platform channels for native code integration (MethodChannel, EventChannel)
  • Testing: unit, widget (testWidgets, pumpWidget), integration, and golden tests
  • Performance: const widgets, ListView.builder, RepaintBoundary, DevTools profiling

??? example "Example usage" Product list with search and pull-to-refresh: Creates a StatefulWidget with a search TextField, uses ListView.builder for efficient rendering, implements RefreshIndicator for pull-to-refresh, fetches products from a repository, and shows loading/error/empty states.

seo-optimization​

SEO optimization including on-page SEO, technical SEO, Core Web Vitals, structured data, and mobile-first indexing. Use when optimizing websites for search engines, implementing structured data, or improving page performance.

Triggers: When optimizing a website for search engines, working with meta tags, structured data, page speed, Core Web Vitals, or mobile-first indexing. Tools: Bash(curl:*) Bash(lighthouse:*) Read Write References: technical-seo-checklist.md

Key capabilities:

  • On-page SEO: title tags, meta descriptions, heading hierarchy, alt text, internal links, URL structure
  • Technical SEO: robots.txt, XML sitemaps, canonical URLs, hreflang, redirect chains, index control
  • Structured data with JSON-LD: Article, Product, FAQ, BreadcrumbList, Organization schemas
  • Core Web Vitals optimization: LCP (<2.5s), INP (<200ms), CLS (<0.1) with specific fix strategies
  • Page speed: render-blocking resources, image compression (WebP/AVIF), CDN, minification, Brotli/gzip
  • Mobile-first indexing: responsive design, viewport meta, touch targets, content parity
  • Internal linking strategy: content hubs, descriptive anchor text, orphan page detection
  • Security and trust signals: HTTPS, HSTS, E-E-A-T authorship

??? example "Example usage" SEO audit of a Next.js site: Checks meta tags on key pages, verifies sitemap.xml and robots.txt, validates structured data with Rich Results Test, runs Lighthouse for Core Web Vitals, checks canonical URLs, and produces a prioritized list of fixes sorted by expected impact.