Productivity skills

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Display project status, active tracks, and next actions

Automate Wrike project management via Rube MCP (Composio): create tasks/folders, manage projects, assign work, and track progress. Always search tools first for current schemas.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Self-improving tool discovery for AI agents. Install one MCP server. Your agent finds the rest. npm · GitHub · Contributing Forage is an MCP server that lets AI agents discover, install, and learn to use new tools — automatically. When an agent hits a wall, it forages for the right tool, installs it, and teaches itself how to use it. No restarts. No manual config. The agent gets permanently smarte

No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points. This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power

This project implements a sophisticated multi-agent research system based on the concepts described in Anthropic's article: Building a Multi-Agent Research System. The system leverages the power of coordinated AI agents to handle deep and complex research queries that require multiple perspectives and iterative investigation. The system is built around a Lead Research Agent that orchestrates the e

Use this skill when working with Conductor's context-driven

A task management system built for AI agents. Official website: https://claudiacli.com/ Claudia provides structured task tracking with a Model Context Protocol (MCP) server, enabling AI assistants like Claude to manage their own work through hierarchical tasks, dependencies, sprints, and acceptance criteria verification. AI agents need a way to: - Track progress across complex, multi-step tasks -

Automate ClickUp project management including tasks, spaces, folders, lists, comments, and team operations via Rube MCP (Composio). Always search tools first for current schemas.

This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.

PAI exists to solve the P0 problem in the world: activating human creative potential through AI. An open-source personal AI infrastructure that makes the best AI accessible to everyone, not just the rich or technical elite.

Smart context management for LLM development workflows. Share relevant project files instantly through intelligent selection and rule-based filtering. Getting the right context into LLM conversations is friction-heavy: - Manually finding and copying relevant files wastes time - Too much context hits token limits, too little misses important details - AI requests for additional files require manual

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur

Fast, opinionated TUI for AI-assisted coding. McGravity orchestrates AI coding tools (Claude Code, Codex, Gemini) in a tight feedback loop: plan small tasks → execute one at a time → review → replan. Instead of dumping a huge prompt and hoping for the best, McGravity breaks your work into atomic pieces that are easier to verify and course-correct. McGravity runs a continuous improvement loop: McGr

Expert Haskell engineer specializing in advanced type systems, pure

A Model Context Protocol (MCP) server that provides access to all the curated awesome lists and their items. It can provide the best resources for your agent from sections of the 8500+ awesome lists on github and more then 1mn+ (growing) awesome row items. What are Awesome Lists? Awesome lists are community-curated collections of the best tools, libraries, and resources on any topic - from machine

Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.

A vibe-coded memory management system with RAG capabilities for Claude via the Model Context Protocol (MCP). SimpleMem is an MCP server that provides persistent memory storage and retrieval for Claude and other MCP clients. It combines traditional file-based storage with modern RAG (Retrieval-Augmented Generation) capabilities, including semantic search and automatic relationship discovery. Think

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

Checks Agent Skills (SKILL.md folders) for portability and common mistakes with a standard-library Python script, then explains each finding and the fix. It catches frontmatter that strict YAML parsers reject (such as an unquoted colon in the description, which makes tools like npx skills skip the skill), keys other than name and description, bad name format, a description that does not say when to use the skill, vendor tool names and paths, missing fallbacks when a tool or the network is...

Use when working with code refactoring context restore