AI & LLM Integration for Claude Code

Start here for any Zoom integration or app idea. Use when you need to choose the right Zoom surface, shape the architecture, or route into the correct implementation skill without reading the whole Zoom doc set first.

Zoom Video SDK for Web - JavaScript/TypeScript integration for browser-based video sessions, real-time communication, screen sharing, recording, and live transcription

Zoom Video SDK for Windows - C++ integration for video sessions, raw audio/video capture, screen sharing, recording, and real-time communication

Zoom Virtual Agent SDK for web embeds. Use for campaign or entry ID chat launch, event-driven controls, user context updates, and CSP-safe deployment.

| Zoom Meeting SDK for Web - Embed Zoom meeting capabilities into web applications. Two integration options: Client View (full-page, familiar Zoom UI) and Component View (embeddable, Promise-based API). Includes SharedArrayBuffer setup for HD video, gallery view, and virtual backgrounds.

| Zoom Meeting SDK Web - Component View. Embeddable Zoom meeting components with Promise-based API for flexible integration. Ideal for React/Vue/Angular apps and custom layouts. Uses ZoomMtgEmbedded with async/await patterns and embeddable UI containers.

Reference skill for Zoom RTMS. Use after routing to a live-media workflow when processing real-time audio, video, chat, transcripts, screen share, or contact-center voice streams.

| Zoom Video SDK for Flutter. Use when building custom video session apps in Flutter with flutter_zoom_videosdk, event-driven architecture, session lifecycle handling, and mobile platform integration patterns.

| Zoom Video SDK for React Native. Use when building custom mobile video session experiences with @zoom/react-native-videosdk, event listeners, helper-based APIs, and backend JWT token flows.

| Zoom Video SDK for Unity wrapper integrations. Use when building custom Unity-based video session experiences and mapping Unity scene/UI state to Video SDK events.

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for C

Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-

Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agen

Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

/cs:ai-act-readiness <system> — EU AI Act 6-question forcing interrogation. Use during AI-system intake, before EU deployment, or during annual compliance refresh as Article 113 obligations phase in (2025-02-02 / 2025-08-02 / 2026-08-02 / 2027-08-02).

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

/cs:aims-audit <scope> — ISO/IEC 42001 AIMS internal-audit 6-question forcing interrogation. Use before certification stage 1, before annual internal audit cycles, or when onboarding a new AI system into an existing AIMS.

Use when the user wants more human-like AI responses — less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', 'less AI', 'talk like a person', 'mirror mode', 'stop being so AI', or when conversations are emotionally charged (grief, job loss, relationship advice, fear). NOT for technical questions, code generation, or factual lookups.

/cs:caio-review <plan> — Eval-demanding Chief AI Officer interrogation of any plan that involves AI: model selection, risk classification, cost economics, or AI hiring. Use when shipping an AI feature without an eval set, choosing between API, fine-tune, and self-hosted, or classifying a use case under the EU AI Act.

Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires, or when user mentions CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act,

Agent Development Guide

/cs:aeo — Answer Engine Optimization workflow. Audit content for E-E-A-T + structure signals that drive LLM citation (ChatGPT, Perplexity, Claude. Slash command for Claude Code, Codex CLI, Gemini CLI.

/cs:aeo — Answer Engine Optimization workflow. Audit content for E-E-A-T + structure signals that drive LLM citation (ChatGPT, Perplexity, Claude, Gemini, Mistral). Optimize content in 3 modes (conservative/balanced/aggressive). Track which LLMs cite which pages via local ledger. Industry-aware thresholds (8 industries with YMYL calibration). Distinct from SEO — refuses to optimize one at expense of the other.

Answer Engine Optimization (AEO) specialist agent. Use when content needs to be optimized for citation by AI language models (ChatGPT, Perplexity. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

EU AI Act (Regulation (EU) 2024/1689) Article-cited compliance operator. Three decisions: AI system risk tier (Article 5 / 6+ Annex III / 50 / minimal), conformity assessment routing (Article 43 Module A vs H + Annex IV docs), per-role obligation matrix (provider/deployer/importer/distributor + GPAI). NOT executive AI strategy (see cs-caio-advisor). NOT a legal substitute (engage counsel for novel cases).

ISO/IEC 42001:2023 AI Management System (AIMS) implementation + internal audit operator. Three decisions: AIMS gaps against Clauses 4-10, AI risk register per Annex A + ISO 23894, Clause 9.2 internal audit plan. NOT executive AI strategy (see cs-caio-advisor). NOT EU AI Act conformity (see cs-ai-act-compliance).

Marc Andreessen-mode operator. Runs on a fixed anti-sycophancy operating prompt — leads with the strongest counterargument, never validates premises. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Eval-demanding Chief AI Officer advisor for model build-vs-buy decisions, AI risk classification under EU AI Act + US state laws, AI cost economics (API vs self-hosted), and AI team org evolution. Strategic only — does not duplicate engineering AI/ML skills.

Eval-demanding Chief AI Officer advisor for model build-vs-buy decisions, AI risk classification under EU AI Act + US state laws, AI cost economics. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

/cs:caveman — Activate persistent caveman-mode. Ultra-compressed responses with technical substance preserved. Auto-clarity exception for warnings + destructive ops. Stays active until 'stop caveman' / 'normal mode'.

Caveman-mode operator. Persistent ultra-compressed communication mode. Drops articles, filler, pleasantries, and hedging while preserving all technical substance. Auto-clarity exception for security warnings, irreversible actions, multi-step sequences, and clarification requests. Activated by user phrases ("caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief") or /cs:caveman command.

Caveman-mode operator. Persistent ultra-compressed communication mode. Drops articles, filler, pleasantries, and hedging while preserving all. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Use proactively after any user message in a Claude.ai or Claude Code session where the user is learning to prompt better or has explicitly activated. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Use proactively after any user message in a Claude.ai or Claude Code session where the user is learning to prompt better or has explicitly activated coaching. Default persona for the claude-coach skill. Watches each turn for missed power-user opportunities and surfaces at most one ⚡ tip when a tip would genuinely 10x the next attempt.

Activate the claude-coach skill — personal Claude power-user coaching for the rest of the conversation.. Slash command for Claude Code, Codex CLI, Gemini CLI.

Compile a goal into a verified agent-harness loop for a domain and drive it to close — /cs:harness <domain> <goal>

NotebookLM browser-automation persona. Walks 2-4 forcing intake questions (Q1 action: read / add source / Studio output / create new; Q2-Q4 branch. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

NotebookLM browser-automation persona. Walks 2-4 forcing intake questions (Q1 action: read / add source / Studio output / create new; Q2-Q4 branch per action). Refuses to start without action commitment. Refuses to handle login flows automatically. Refuses to wait synchronously for Studio generations (fire-and-notify). Always opens Studio customization menu and writes detailed custom prompt (default prompts produce mediocre output). Screenshot-first discipline. find()-before-click. Fails fast wh