AI & LLM Integration for Claude Code

对AI应用场景按登记册分类，开展算法安全评估和科技伦理审查，审查AI供应商合同条款（训练数据、责任、模型变更等），保持AI政策与最新实践同步。适配生成式人工智能服务管理办法、科技伦理审查办法。

Skills for integrating Resend email service, enabling AI-assisted email campaign creation and management.

Channel plugin for Android Remote Control MCP — receives device events and pushes them into Claude Code sessions.

Agent skills for automating WhatsApp interactions and workflows. Enables sending messages, managing conversations, and automating messaging-based processes.

Vibes is for people who use Claude Code, but don't know how to code.

Slack command that embeds healthy skepticism into AI dialogue by countering confirmation bias and authority bias. Encourages critical evaluation of AI responses.

Drop-in agentic loop skills (autoresearch, literature & writing, data, optimization, red-team, power analysis). Skills install namespaced as agent-loops:<name>.

MCP server plugin for voice input and output in Claude Code, enabling voice-based interaction with coding assistants.

Comfy Cloud in Claude Code: the hosted MCP connection plus slash commands for generating images, video, audio, and 3D and searching the catalog.

Prompt engineering skill for Claude Code, OpenClaw, Codex, Grok CLI, and Hermes Agent

Capture a writer's voice from a few sample articles and rewrite text to sound like that author. Works for any author, 王建硕 included.

Time-aligned TTS dub of a video against a target-language SRT. Volcano TTS for Chinese, edge-tts for everything else. Single-speaker by default; opt-in visual diarization for multi-speaker.

wjs-evaling-voicedrop-prompts

Audio/video → timestamped SRT in the source language. Chinese uses Volcano ASR; other languages use OpenAI Whisper with word-level timestamps + punctuation-bounded re-segmentation.

VoiceDrop 配套 skill。两个模式 — (1) list：列出你在 VoiceDrop R2 上的全部文件（录音 / 文章 JSON / 标记）；(2) distill：从你的样本文章蒸馏文风，生成可贴入 VoiceDrop「设置→文风」的精简规则集，并可直接通过 Files API 上传。触发词："voicedrop list"、"列出 VoiceDrop 文件"、"voicedrop distill"、"蒸馏 VoiceDrop 文风"、"/wjs-voicedrop"。

Comprehensive red teaming methodology for both cybersecurity (MITRE ATT&CK, pentesting) and AI/LLM systems (prompt injection, jailbreaking, OWASP Top 10 LLM). Includes threat modeling, attack simulation, compliance validation (NIST, EU AI Act), and progressive attack technique references.

Agent capability frameworks: STAR framework for understanding implicit intent (framework-initiative), critical thinking architecture with reasoning selection (framework-critical-thinking), and adversarial review protocol with mandatory bug quota (adversarial-review). Build smarter, self-correcting agents and validate plans before execution.

Enables texting Claude directly from your iPhone via iMessage for on-the-go conversational AI access.

Help coding agents integrate with the Shuriken trading platform via its API, SDK, and CLI.

WakaTime plugin for Claude Code - Track your AI usage, lines of code generated, and time spent prompting AI

Audio processing skill for transcription, summarization, and content extraction from voice recordings and audio files.

SwiftUI preview integration for AI coding. Capture, review, and iterate on SwiftUI views using axe CLI.

Enhanced web browsing for Claude Code. Handles search, scraping, login flows, and dynamic pages via Playwright. Use for any task requiring real browser interaction.

Gives Claude Code a multi-agent coordination layer over MCP — an atomic shared blackboard (locked propose/validate/commit), permission gating with HMAC/Ed25519-signed tokens, federated token-budget tracking, and append-only audit-log queries. Backed by an orchestrator with 32 framework adapters (LangChain, AutoGen, CrewAI, Gemini, and more).

Real-time communication and voice agent integration with LiveKit. Enables AI agents to participate in video, audio, and messaging sessions.

Self-learning AI agents that improve over time. Features agent memory (tracks last 100 executions), pattern recognition, community standards contribution, and workflow optimization. Includes security, quality, deployment, infrastructure, and compliance workflows.

AI Operations Team — 9 agents: Deploy, Eval, Trace, Guard, Budget, Token, Prompt, Embed, Rank

AI Operations Team — Budget: LLM spend tracking, model cost optimization, budget alerts, and token efficiency audits across AI workloads.

Design cost reduction strategies — model tiering, prompt compression, caching, batch inference.

Contribute a discovery back to the upstream tonone repo — new skill, improved agent prompt, better routing rule. Use when you hit a gap, found a better pattern, or built something reusable that others would benefit from.

ML/AI engineer — model training, MLOps, feature engineering, LLM integration

Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".

Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".

Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".

ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".

AI Operations Team — Deploy: Model serving, inference APIs, blue/green deploys, rollback, and canary releases for production ML.