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Found 2,161 Skills
Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Integrate with Affinda's document AI API to extract structured data from documents (invoices, resumes, receipts, contracts, and custom types). Covers authentication, client libraries (Python, TypeScript), structured outputs with Pydantic models and TypeScript interfaces, webhooks, upload patterns, and the full documentation map. Use when building integrations that parse, classify, or extract data from documents using Affinda.
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.
Create and use brand.yml files for consistent branding across Shiny apps and Quarto documents. Use when working with brand styling, colors, fonts, logos, or corporate identity in Shiny or Quarto projects. Covers: (1) Creating new _brand.yml files from brand guidelines, (2) Applying brand.yml to Shiny for R apps with bslib, (3) Applying brand.yml to Shiny for Python apps with ui.Theme, (4) Using brand.yml in Quarto documents, presentations, dashboards, and PDFs, (5) Modifying existing brand.yml files, (6) Troubleshooting brand integration issues. Includes complete specifications and framework-specific integration guides.
Enforces using shell script one-liners as the primary approach for scripts. Prohibits Node.js and Python usage. Use TypeScript with Deno only when variables or complex branching are necessary. MUST ALWAYS be applied when creating scripts, automation tasks, or executing commands.
This skill should be used when building data processing pipelines with CocoIndex v1, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex v1 is Python-native (supports any Python types), has no DSL, and is currently under pre-release (version 1.0.0a1 or later).
Detect Single Responsibility Principle (SRP) violations using multi-dimensional analysis. Use when reviewing code for "SRP", "single responsibility", "god class", "doing too much", "too many dependencies", before commits, during refactoring, or as quality gate. Analyzes Python, JavaScript, TypeScript files with AST-based detection, metrics (TCC, ATFD, WMC), and project-specific patterns. Provides actionable fix guidance with refactoring estimates.
Add observability to any repo: Sentry (errors), PostHog (analytics), Helicone (LLM costs). Auto-detects language/framework. Creates Sentry project via MCP. Installs SDKs, writes config, updates .env.example, opens PR. Supports: Next.js, Node/Express/Hono, Go, Python, Swift, Rust, React Native.