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Found 270 Skills
Expert guide for the NotebookLM CLI (`nlm`) - a command-line interface for Google NotebookLM. Use this skill when users want to interact with NotebookLM programmatically, including: creating/managing notebooks, adding sources (URLs, YouTube, text, Google Drive), generating content (podcasts, reports, quizzes, flashcards, mind maps, slides, infographics, videos, data tables), conducting research, chatting with sources, or automating NotebookLM workflows. Triggers on mentions of "nlm", "notebooklm", "notebook lm", "podcast generation", "audio overview", or any NotebookLM-related automation task.
Comprehensive codebase research skill. Documents codebase as-is by spawning parallel sub-agents and synthesizing findings into research documents.
Replace generic perspectives with domain-specific expert roles selected dynamically per request. Automatically picks the 3 most relevant experts from a role pool (Security, Performance, UX, Cost, DX, Architecture, etc.) based on the task context.
AI agent configuration policy and security guide. Project description file writing, Hooks/Skills/Plugins setup, security policy, team shared workflow definition.
Configure and orchestrate Claude Code agent teams (TeamCreate, SendMessage, TaskUpdate workflow). Use when you need multiple agents working in parallel on a complex task, want to coordinate background agents with messaging, or are setting up a lead/teammate architecture with a shared task list. Teams are experimental — enable with --enable-teams flag.
Audit and optimize Claude Code configuration with dynamic best-practice research
Review a skill and extract deterministic, mechanical steps into shell scripts. Makes skills more reliable by separating precision work (scripts) from judgment work (AI). Use when asked to extract scripts from a skill, make a skill more deterministic, or split a skill into script + prompt.
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Multi-agent swarm orchestration where AI agents spawn, coordinate, and self-organize into collaborative teams. Use when running parallel AI agent tasks, orchestrating multi-agent workflows across Claude Code / Codex / Cursor / custom agents, isolating agent workspaces via git worktrees, tracking task dependencies across agents, or running autonomous experiments. Triggers on: clawteam, agent swarm, spawn agents, multi-agent team, agent orchestration, parallel agents, agent coordination, swarm intelligence, agent spawn, clawteam spawn, agent worktree, agentic team, ml agent experiments, autonomous agents, agent team.
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
Manages the ai-context/ memory layer: initialize from scratch, update with session work, or maintain/cleanup. Trigger: /memory-init, /memory-update, /memory-maintain, initialize memory, update memory, maintain memory.
Multi-agent board meeting protocol for strategic decisions. Runs a structured 6-phase deliberation: context loading, independent C-suite contributions (isolated, no cross-pollination), critic analysis, synthesis, founder review, and decision extraction. Use when the user invokes /cs:board, calls a board meeting, or wants structured multi-perspective executive deliberation on a strategic question.