项目地址
https://github.com/anthropics/cwc-workshops
AI 摘要
该仓库包含 Anthropic 主办的 Code with Claude 工作坊 材料，涵盖多个主题：通过 SKILL 审计 LLM 评估套件以选择最优模型；将 400 行提示的库存代理分解为多智能体系统；AI 辅助产品工作流的三阶段实践；构建流式事件驱动的 SRE 事故仪表板代理；45 分钟配置游戏机器人竞赛；为代理添加跨会话记忆；通过评估驱动迭代生成 PPTX 的代理；构建多智能体并购研究团队；以及搭建 SEC 文件研究平台。所有材料不维护、不接受贡献，基于 Apache 2.0 许可。
README 原文
cwc-workshops
Workshop materials. Not maintained and not accepting contributions.
Materials from Anthropic-run Code with Claude workshops.
Workshops
rightmodel/ — Picking the Right Model: use a Claude Code SKILL to audit an LLM eval suite and sweep it across models and inference parameters (extended thinking, effort) to find the best quality-per-dollar and quality-per-second configuration.agent-decomposition/ — Compose Multi-Agent Systems with Skills and MCP: decompose a 400-line-prompt inventory agent into skills + code execution + callable_agents on Claude Managed Agents, with evals to verify each step.how-we-claude-code/ — How We Claude Code: a three-phase walkthrough of an AI-assisted product workflow — interview to spec, four divergent design explorations as static HTML, and a Vite + React app whose components emit a machine-readable DOM contract so an agent (or CI) can verify them at runtime.ship-your-first-managed-agent/ — Ship Your First Managed Agent: a Streamlit incident dashboard with an offline SRE Agent chat panel. You bring it online by implementing seven small functions in agent.py, each a single Claude Managed Agents API call — until it can grep a 70k-line log in its sandbox, call your local tools, and name the bad commit.agent-battle/ — Agent Battle: a 45-minute competition to configure a Claude Managed Agent — system prompt, skills, MCP servers, model — that drives a local game bot over MCP. Most diamonds wins, fewest tokens breaks ties; a fast --eval decision-probe loop lets you test config changes in ~30s before committing to a 5-minute run.agents-that-remember/ — Agents That Remember: start with a Managed Agent that's visibly amnesiac across sessions, then layer in...
项目地址
https://github.com/anthropics/cwc-workshops
AI 摘要
该仓库包含 Anthropic 主办的 Code with Claude 工作坊 材料，涵盖多个主题：通过 SKILL 审计 LLM 评估套件以选择最优模型；将 400 行提示的库存代理分解为多智能体系统；AI 辅助产品工作流的三阶段实践；构建流式事件驱动的 SRE 事故仪表板代理；45 分钟配置游戏机器人竞赛；为代理添加跨会话记忆；通过评估驱动迭代生成 PPTX 的代理；构建多智能体并购研究团队；以及搭建 SEC 文件研究平台。所有材料不维护、不接受贡献，基于 Apache 2.0 许可。
README 原文
cwc-workshops
Workshop materials. Not maintained and not accepting contributions.
Materials from Anthropic-run Code with Claude workshops.
Workshops
rightmodel/— Picking the Right Model: use a Claude Code SKILL to audit an LLM eval suite and sweep it across models and inference parameters (extended thinking, effort) to find the best quality-per-dollar and quality-per-second configuration.agent-decomposition/— Compose Multi-Agent Systems with Skills and MCP: decompose a 400-line-prompt inventory agent into skills + code execution + callable_agents on Claude Managed Agents, with evals to verify each step.how-we-claude-code/— How We Claude Code: a three-phase walkthrough of an AI-assisted product workflow — interview to spec, four divergent design explorations as static HTML, and a Vite + React app whose components emit a machine-readable DOM contract so an agent (or CI) can verify them at runtime.ship-your-first-managed-agent/— Ship Your First Managed Agent: a Streamlit incident dashboard with an offline SRE Agent chat panel. You bring it online by implementing seven small functions inagent.py, each a single Claude Managed Agents API call — until it can grep a 70k-line log in its sandbox, call your local tools, and name the bad commit.agent-battle/— Agent Battle: a 45-minute competition to configure a Claude Managed Agent — system prompt, skills, MCP servers, model — that drives a local game bot over MCP. Most diamonds wins, fewest tokens breaks ties; a fast--evaldecision-probe loop lets you test config changes in ~30s before committing to a 5-minute run.agents-that-remember/— Agents That Remember: start with a Managed Agent that's visibly amnesiac across sessions, then layer in...