Apify Actors Development Guide
Talent Scout is an AI-powered Apify Actor for automated candidate sourcing. It scrapes LinkedIn, GitHub, and other platforms, then uses LLMs to rank and evaluate developer profiles against job requirements.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
497 skills found
Talent Scout is an AI-powered Apify Actor for automated candidate sourcing. It scrapes LinkedIn, GitHub, and other platforms, then uses LLMs to rank and evaluate developer profiles against job requirements.
Control Claude Code via MCP protocol for autonomous development. Features persistent sessions, agent teams, precise execution planning, and advanced tool management for complex coding tasks.
Senior backend architecture expert specializing in Hexagonal Architecture, DDD, SOLID principles, clean code, and refactoring to guide development, reviews, and architectural problem-solving.
Execute the implementation planning workflow, generate technical design artifacts, and structure research tasks for Spec Kit projects.
Profiles application performance using k6, Artillery, or JMeter to measure latency, throughput, and error rates. Ideal for planning load, stress, and soak tests to identify bottlenecks.
Advanced context engineering system for orchestrating AI agents, memory management, and token optimization to improve long-term persistence and project intelligence.
Implement an AI agent delegation architecture to keep your main context clean, reduce token costs, and isolate specialized infrastructure or API tasks.
Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.
Orchestrates Change Request Document workflows for brownfield projects, managing codebase context, impact analysis, and CRD document generation.
A comprehensive framework for creating, structuring, and managing reusable AI Agent Skills to standardize instruction-driven workflows.
Executes Gradle-based Java tests, filters results for failures and key statistics, and provides concise reports to streamline backend development and debugging.
Execute implementation plans in small, verifiable batches with pause-for-feedback checkpoints to prevent drift and ensure code quality.