Skill UI

Defuddle is a CLI tool to extract clean Markdown from HTML web pages. It removes navigation, ads, and clutter, reducing token usage. Users can output Markdown, JSON, or HTML, and extract metadata. Preferred over WebFetch for standard pages, it provides cleaner content for processing and analysis.

Master the art of building robust, production-grade LLM chains using LangChain Expression Language (LCEL). This guide covers advanced composition patterns, including sequential, parallel, and conditional branching chains. It also details prompt templating, structured output parsing (JSON/Zod), and context injection, enabling the creation of complex, multi-step AI pipelines.

A comprehensive workflow tool for managing the entire localization process using Lokalise. It handles downloading translated files (JSON, XLIFF, PO), reviewing translation status, updating keys, and integrating the output directly into development applications. Ideal for CI/CD pipelines and developers needing to sync localized content efficiently.

This comprehensive workflow guide covers all production-ready patterns for Mistral AI chat completions. It demonstrates critical advanced features including multi-turn conversation management, real-time streaming responses, structured JSON output using schemas, and implementing robust guardrails and moderation. Ideal for building scalable, production-grade conversational AI applications and advanced chat interfaces.

This guide provides production-ready patterns for integrating the Mistral AI SDK in both TypeScript and Python. It covers essential best practices such as singleton client management, handling structured JSON output, implementing streaming responses, concurrent asynchronous processing, and robust error handling with exponential backoff. Use this when establishing team coding standards or refactoring Mistral API usage.

Basic Text Processor provides word/character statistics, simple text transformations, batch file handling, and dual JSON or human-readable outputs to demonstrate a CLAUDE skill template.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

Parallel Web Research skill orchestrates Parallel Chat and Extract APIs to search the internet, perform deep intelligence work, and craft cited summaries, with model controls, JSON outputs, and citation verification for market, news, technical, and general queries.

Intelligent multi-strategy web scraper that selects between static WebFetch, browser automation, curl/bash, and WebSearch paths to extract structured tables, lists, pricing, and other data, with pagination handling, validation confidence, transforms, and CSV/JSON output for monitoring workflows.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.