[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-11330":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":8,"htmlUrl":8,"language":9,"languages":8,"totalLinesOfCode":8,"stars":10,"forks":11,"watchers":12,"openIssues":13,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":15,"stars7d":16,"stars30d":17,"stars90d":14,"forks30d":14,"starsTrendScore":18,"compositeScore":19,"rankGlobal":8,"rankLanguage":8,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":8,"pushedAt":8,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":14,"starSnapshotCount":14,"syncStatus":28,"lastSyncTime":29,"discoverSource":30},11330,"cwc-workshops","anthropics\u002Fcwc-workshops","anthropics",null,"TypeScript",925,249,14,1,0,104,128,747,312,11.19,"Apache License 2.0",false,"main",true,[],"2026-06-12 02:02:31","\u003C!-- Copyright 2026 Anthropic PBC -->\n\u003C!-- SPDX-License-Identifier: Apache-2.0 -->\n\n# cwc-workshops\n\nWorkshop materials. Not maintained and not accepting contributions.\n\nMaterials from Anthropic-run **Code with Claude** workshops.\n\n## Workshops\n\n- [`rightmodel\u002F`](.\u002Frightmodel) — *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.\n- [`agent-decomposition\u002F`](.\u002Fagent-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.\n- [`how-we-claude-code\u002F`](.\u002Fhow-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.\n- [`ship-your-first-managed-agent\u002F`](.\u002Fship-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.\n- [`agent-battle\u002F`](.\u002Fagent-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.\n- [`agents-that-remember\u002F`](.\u002Fagents-that-remember) — *Agents That Remember*: start with a Managed Agent that's visibly amnesiac across sessions, then layer in memory primitives one at a time — a memory store for cross-session persistence, then the Dreaming Service to consolidate past transcripts — going \"goldfish to colleague\" in 45 minutes.\n- [`eval-driven-agent-development\u002F`](.\u002Feval-driven-agent-development) — *Eval-Driven Agent Development*: iterate a PPTX-generating Managed Agent through six variants (naive → visual → typography → palette → density → QA-loop), scoring each against a 10-task suite with a two-layer grader (programmatic `.pptx` XML metrics + LLM-as-judge on rendered slides) so every prompt change is measured, not vibed.\n- [`production-ready-agent\u002F`](.\u002Fproduction-ready-agent) — *Deal Desk*: a chat-first UI over a multi-agent M&A research team on Claude Managed Agents — a coordinator delegates to four parallel research sub-agents, reads prior-deal lessons from a memory store, reaches Linear via MCP, and emits a graded investment thesis while the UI streams every event and gated tool call.\n\n## License\n\nApache License 2.0. See [LICENSE](.\u002FLICENSE).\n","cwc-workshops 是一套由 Anthropic 组织的 Code with Claude 工作坊材料，旨在通过一系列实际操作来展示如何使用 Claude AI 模型进行开发。该项目涵盖了从选择合适的模型到构建生产级代理的多个方面，包括评估不同配置下的性能、分解多代理系统、AI 辅助的产品工作流以及记忆机制等核心功能。它利用了 TypeScript 语言，并且强调了对 Claude Managed Agents API 的调用和技能组合。适合于希望深入了解并实践基于 Claude AI 模型的应用开发场景，特别是那些关注效率与成本优化、多代理协作及持续集成验证的开发者。",2,"2026-06-11 03:31:39","CREATED_QUERY"]