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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.

A comprehensive toolkit for navigating, optimizing, and managing large monorepos built with JavaScript/TypeScript. It supports industry-leading tools like Turborepo, Nx, pnpm, and Lerna. Key features include cross-package impact analysis, selective builds, remote caching, dependency graph visualization, and structured migrations, ensuring fast, reliable CI/CD pipelines for complex codebases.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

Comprehensive guide and expert assistance for building, refactoring, and validating AWS cloud infrastructure using the AWS Cloud Development Kit (CDK). Covers best practices for TypeScript and Python, including resource naming conventions, Lambda function development, and multi-layer pre-deployment validation using tools like cdk-nag.

Analyzes a code repository to generate a recommended, minimal set of read-only shell command permissions for Claude Code's settings.json. It automatically detects the project's tech stack (e.g., Python, Node.js, Go), build tools, and monorepo structure, ensuring a safe and evidence-based security baseline for new projects or auditing existing permissions.

This comprehensive framework provides C-suite leaders (COO) with robust tools to translate high-level corporate strategy into actionable operational execution. It guides users in designing optimized business processes, establishing operational cadences, tracking OKRs, and systematically identifying and resolving bottlenecks inherent in rapid company scaling. Ideal for improving operational efficiency and organizational maturity.

Provides charting, mapping, and dashboard techniques to turn datasets into clear insights for sharing across teams.

This skill automates the creation of isolated, pre-configured development containers (devcontainers). It scans the project structure to detect supported languages (Python, Node.js, Rust, Go) and intelligently generates the necessary configuration files, VS Code extensions, and setup scripts. It ensures all required tools are installed in a sandboxed environment, making it ideal for starting new multi-language projects or needing consistent, reproducible development workspaces.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

Guides growth engineers through evaluating, designing, and launching free marketing tools that drive traffic, leads, and backlinks without paid ads, covering idea scoring, UX, lead capture, and launch tactics.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.