Browse skills

Traefik — provides AI agent assistance for software development workflows.

Php — provides AI agent assistance for software development workflows.

Coordinate multi-agent development pipelines using shared JSON task files. Supports Linear, DAG, and Debate modes for AI agent orchestration across code, test, docs, and monitor agents.

Persistent local memory for AI agents. Save, recall, and search project decisions as local JSON. Zero cloud, zero infrastructure — works offline.

Three-layer progressive MCP client with Socket daemon. Query available tools, call MCP servers efficiently, and manage daemon processes. Reduces token overhead with lazy loading.

Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to 6 specialists, generates health score.

Zustand manages complex application state efficiently, simplifying development and improving data consistency across your AI agent's interactions.

Provides MCP tools guidance and assistance for orchestrating AI agents and multi-step workflows.

Autonomous AI coding with spec-driven development. Implements Geoffrey Huntley's iterative bash loop methodology where agents work through specs one at a time, outputting a completion signal only when acceptance criteria are 100% met.

The I18N skill translates user input and agent responses across multiple languages, enhancing global accessibility and communication.

Provides MCP tools guidance and assistance for orchestrating AI agents and multi-step workflows.

Provides MCP tools guidance and assistance for orchestrating AI agents and multi-step workflows.

This AI agent automates trading on Uniswap by analyzing liquidity pools and executing trades based on pre-defined strategies for profit maximization.

Dynamically assigns tasks to multiple agents, optimizing workflow and resource allocation for complex objectives.

Provides MCP tools guidance and assistance for orchestrating AI agents and multi-step workflows.

Provides MCP tools guidance and assistance for orchestrating AI agents and multi-step workflows.

Parcadei's agent-orchestration dynamically chains and manages multiple AI agents to achieve complex tasks efficiently.

Grpo RL Training facilitates rapid reinforcement learning model development by automating and optimizing agent training processes for improved performance.

Parcadei's agent-context-isolation prevents unintended knowledge leakage between agent conversations and tasks.

Decompose complex tasks into specialized sub-agents for improved efficiency and nuanced problem-solving capabilities.

Quickly gathers relevant information from web sources to answer questions or support decision-making tasks.

Generates detailed, step-by-step plans to achieve complex goals, optimizing actions and anticipating potential roadblocks.

Periodically checks external services & APIs to proactively identify and resolve potential issues impacting agent performance.

Refines agent workflows and capabilities through iterative feedback loops and automated optimization techniques.