# Claude Workshop
> Official Claude Code ambassadors delivering hands-on workshops and AI coding training for engineering teams.


## When to use this
- Use Claude Workshop for team-wide Claude Code adoption, repository instruction design, safe delegation, AI-assisted code review, MCP boundaries, and rollout planning.
- Use the training pages to match a team to onsite, virtual, or offsite delivery. Use the methodology page for the Delegate, Review, Own operating model.
- Call public discovery, Markdown pages, OpenAPI, and the read-only sandbox without credentials. Start at https://www.claudeworkshop.com/developers.
- Escalate to a person before making claims about price, dates, certification, client results, private code, or production API access. Book at https://harness.institute/call or email info@claudeworkshop.com.
- Do not use Claude Workshop as general Claude product support, and do not send secrets, source code, or personal data to public endpoints.

## How an agent should call this site
1. Read https://www.claudeworkshop.com/llms.txt and https://www.claudeworkshop.com/sitemap.xml.
2. Request the text/markdown media type on supported public pages when compact text is useful.
3. Inspect https://www.claudeworkshop.com/openapi/site-api.json and https://www.claudeworkshop.com/api/sandbox before calling JSON endpoints.
4. Use https://www.claudeworkshop.com/mcp for MCP discovery. Public tool listing is read-only.
5. Ask for reviewed credentials through https://www.claudeworkshop.com/contact only when public access is not enough.

## CLI
- Official CLI source: https://github.com/cursorworkshop/workshops/tree/main/packages/claudeworkshop-cli
- Package name: claudeworkshop
- Registry status: source ready; npm publication must be verified before an agent uses an npm install command.


## Services
- Team Claude Code Workshops: Hands-on workshops teaching teams to use Claude Code safely and effectively
- AI Coding Review Training: Best practices for code review habits with AI assistance
- Agent Workflow Setup: Shared agent workflows for team adoption

## Docs
- [Homepage](https://www.claudeworkshop.com)
- [Training](https://www.claudeworkshop.com/training)
- [Training topics](https://www.claudeworkshop.com/topics)
- [Methodology](https://www.claudeworkshop.com/methodology)
- [About](https://www.claudeworkshop.com/about)
- [Contact](https://www.claudeworkshop.com/contact)
- [Developer portal](https://www.claudeworkshop.com/developers): API documentation, credential guidance, quickstarts, and read-only sandbox
- [Research index](https://www.claudeworkshop.com/research)
- [Book a 15-minute sync](https://harness.institute/call)
- [Sitemap](https://www.claudeworkshop.com/sitemap.xml)
- [Research feed](https://www.claudeworkshop.com/feed.xml)

## Topic Guides
- [Claude Code subagents and skills for teams](https://www.claudeworkshop.com/topics/subagents-and-skills): Adding more subagents rarely speeds a team up; runs get abandoned and review piles up when nobody owns the result. Effective subagents delegation starts with agent boundaries: which tasks delegate safely, and which instructions, MCP, and skills control each run. This guide covers AI team skills and team AI workflows, plus the skill integration that lets engineers inspect and improve delegated work.
- [Claude Code for engineering teams](https://www.claudeworkshop.com/topics/team-conventions): Running Claude Code with an engineering team works when the team shares conventions before everyone starts prompting differently. The operating model covers CLAUDE.md, MCP access, hooks, review evidence, split-team alignment, and governance. Teams then compare outcomes against a baseline instead of trading anecdotes about which prompt worked.
- [Claude Code CLI: MCP, hooks, and review](https://www.claudeworkshop.com/topics/cli-workflows): AI agents in the terminal move fast, but unreviewed output is the real risk. A reliable CLI workflow starts with a written task brief, a bounded implementation scope, and verification loops the agent runs before it stops. That turns each run into a small, readable diff a person can review. The result is speed your team can trust, because code review stays in human hands.
- [MCP training for engineering teams: servers, skills, workflows](https://www.claudeworkshop.com/topics/mcp-team-skills): MCP (Model Context Protocol) is the open standard that lets AI coding agents call external tools such as databases, ticket systems, and deploy pipelines through MCP servers. That reach widens the blast radius fast: one prompt can touch production, so AI integration safety depends on tight tool permissions and a short list of approved servers. Our MCP training shows teams how to vet each server and keep workflow integration reviewable before anyone wires it in.
- [AI code review governance for engineering leaders](https://www.claudeworkshop.com/topics/ai-coding-governance): Governance fails when it lives in a policy doc instead of the daily pull request. Engineering leaders need an operating model that turns AI code review into a habit: clear LLM code review standards, defined agent permissions for coding agents, and MCP boundaries reviewers can enforce. We build that governance model into task categories teams actually use, with adoption checks that show whether it holds.
- [Agentic coding workshops for engineering teams](https://www.claudeworkshop.com/topics/agentic-coding-workshops): Most teams adopt AI coding tools individually and never build a shared way of working, so output stays inconsistent. This agentic coding workshop trains engineers as a team on real repositories, not demos. You leave with a tested AI coding workflow, clear rules for using coding agents, and a team adoption plan that survives past the first week.
- [Safe AI coding practices for development teams](https://www.claudeworkshop.com/topics/safe-ai-coding-practices): Train a development team to adopt safer AI coding practices by making the workflow concrete: bounded scopes, approved context, verification habits, and review gates before merge. AI coding safety holds when the model knows when it can implement, advise, or hand the call to an engineer, and when reviewers can see the evidence behind every AI-assisted change.
- [AI code review habits for generated code](https://www.claudeworkshop.com/topics/ai-code-review-habits): The best way to implement code review habits for AI-generated code is to review the task brief, diff shape, test evidence, and ownership boundary before merge. AI-generated code can pass tests and read cleanly while hiding context mistakes, weak coverage, or risky abstractions, so reviewers need a repeatable AI code critique workflow rather than a rubber stamp.
- [Shared agent workflows for code review risk reduction](https://www.claudeworkshop.com/topics/shared-agent-workflows): Hands-on workshops for shared agent workflows help mixed-experience teams align on what an AI assistant should touch, what engineers review, and what stays human-owned. Shared workflows reduce code review risk because fast adopters, skeptics, and reviewers use the same delegation boundaries and evidence standards.
- [UK AI coding workshops for engineering teams](https://www.claudeworkshop.com/topics/uk-ai-coding-workshops): Teams that book a generic AI talk rarely change how anyone ships code. This hands-on training runs remotely or onsite for UK and European engineering teams that want practical AI coding adoption, not theory. Exercises follow your real workflow: planning, implementation, code review of AI-generated changes, testing, and documentation, so habits stick after the day ends.
- [Claude Code MCP training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-mcp-training): Most Claude Code MCP setup lives in one engineer's local config, so no one else knows which docs, tools, and repositories the agent can reach. This MCP training shows teams how to define context access as shared rules: which MCP servers connect, what each model context protocol integration exposes, and where the agent's boundaries stop.
- [Claude Code hooks training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-hooks-training): Most teams adopt Claude Code hooks and end up drowning in false alarms because every event fires a check. Hooks training shows engineers how to wire workflow automation to the few standards that matter, run checks before risky actions, and verify edits after they happen. The result is real standards enforcement and agent control without constant noise.
- [Claude Code review training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-review-training): AI assistants generate and critique work fast, but teams still need shared review standards before merging. This guide covers Claude Code review habits that keep changes small and inspectable: how to run effective PR review on AI-written diffs, when a security review is required, and how to use code critique without losing control. Every accepted change stays tied to passing tests and clear evidence.
- [Claude Code plan mode training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-plan-mode-training): Without a planning step, an AI agent can rewrite files before anyone checks its reasoning. Plan mode fixes this by keeping Claude Code read-only until you approve a written plan. Teams learn a planning workflow that puts scope approval before edits, so engineers keep implementation control and own which changes ship.
- [Claude Code best practices training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-best-practices-training): Most Claude Code adoption fails because best practices stay in private habits reviewers cannot see. Standardize team workflow by writing conventions into CLAUDE.md, skills, hooks, and review expectations, so every engineer follows the same standards documentation. This training shows teams how to turn AI coding practices into shared, inspectable rules instead of a growing prompt library.
- [Claude Code course for engineering teams](https://www.claudeworkshop.com/topics/claude-code-course-for-teams): Most Claude Code courses demo features and leave a team with no shared way to work. This Claude Code course is practical training for teams, built around your own code: planning, editing, testing, review, and escalation. Each module of the team workshop produces an artifact people keep using, so skill development holds after the training course ends.
- [Claude Code enterprise training for engineering teams](https://www.claudeworkshop.com/topics/claude-code-enterprise-training): Rolling out Claude Code across an enterprise means more than buying seats. Teams that scale adoption without losing engineering ownership set permissions, define network assumptions, govern MCP access, and add review gates from day one. This training gives engineering teams a Claude Code enterprise rollout path with governance and control built in, so safe adoption grows with the work.

## Research
- [Heimdall Adds Trust Verdicts to Agent Memory](https://www.claudeworkshop.com/research/heimdall-adds-trust-verdicts-to-agent-memory): Heimdall is an open-source knowledge layer that verifies AI coding agent memory hits before an agent acts on them.
- [Epho Runs Claude Code with Curl](https://www.claudeworkshop.com/research/epho-runs-claude-code-with-curl): Epho wraps cloud sandboxes behind one API call, so developers can test coding agents without building the runner.
- [machine0 Puts Agent VMs on the CLI](https://www.claudeworkshop.com/research/machine0-puts-agent-vms-on-the-cli): machine0 gives coding agents persistent CPU and GPU VMs from a CLI. Here is what matters, what is unclear, and how to test it safely.
- [Waku Is a Native Coding-Agent App](https://www.claudeworkshop.com/research/waku-is-a-native-coding-agent-app): Waku is a Rust and GPUI desktop app for coding agents. The useful question is whether native control beats chat.
- [Maximizing Claude Code Sessions](https://www.claudeworkshop.com/research/maximizing-claude-code-sessions): Anthropic’s session-value guidance shows how to spend less context, avoid cache surprises, and compare coding agents fairly.
- [self-bench Turns Private PRs Into Evals](https://www.claudeworkshop.com/research/self-bench-turns-private-prs-into-evals): self-bench turns completed private PRs into coding-agent evals, with a safer way to measure agents on real repo work.
- [/show-me Makes Coding Agents Draw](https://www.claudeworkshop.com/research/show-me-makes-coding-agents-draw): HumanLayer's /show-me turns coding-agent explanations into compact visuals so developers can review shape, flow, and risk faster.
- [Claude Code 2.1.229 Stabilizes Remote Control](https://www.claudeworkshop.com/research/claude-code-2-1-229-stabilizes-remote-control): Claude Code 2.1.229 steadies Remote Control, MCP OAuth, streaming, hooks, and plugin command sources.
- [Simon Willison Ships alchemy-utils Alpha](https://www.claudeworkshop.com/research/simon-willison-ships-alchemy-utils-alpha): Simon Willison’s alchemy-utils 0.1a0 turns an AI-built database spike into a small alpha worth studying.
- [Claude Code User-Agent Email Leak Report](https://www.claudeworkshop.com/research/claude-code-user-agent-email-leak-report): A reported Claude Code curl User-Agent email leak shows why agent-run network commands need explicit header review.
- [Claude Code Makes Auto Mode Default](https://www.claudeworkshop.com/research/claude-code-makes-auto-mode-default): Claude Code now defaults to Auto mode. Here is what changed, why developers cared, and the safest first repo check.
- [Agentic Coding Breaks At The Handoff](https://www.claudeworkshop.com/research/agentic-coding-governance-20260517-0513): Most teams do not lose control when an agent writes bad code. They lose it when nobody can explain the change ten minutes later. The handoff is the interface.
- [Claude Code 2.1.139 team conventions](https://www.claudeworkshop.com/research/claude-team-conventions-20260517-0509): Claude Code 2.1.139 team conventions: a CLAUDE TOC, red-folder approvals, data-class tags on MCP connectors, and a weekly retro note.
- [Best practices for agentic coding in real environments](https://www.claudeworkshop.com/research/agentic-coding-governance-20260516-0456): An operating guide to best practices for agentic coding in real environments: rule-file precedence, scope ledgers, replay receipts, connector cards.
- [claude_code_stop_hook_block_cap in Claude Code 2.1.143](https://www.claudeworkshop.com/research/claude-team-conventions-20260516-0448): What claude_code_stop_hook_block_cap searchers need: the Claude Code 2.1.143 hook change handled as convention, with rollback paths and receipts.
- [Claude Code 2.1.142 team conventions](https://www.claudeworkshop.com/research/claude-team-conventions-20260515-1113): Claude Code 2.1.142 team conventions for parallel agent streams: a skill index, a hook budget, a CLAUDE TOC, and red-folder approvals.
- [Codex workspace agents need repo rules](https://www.claudeworkshop.com/research/agentic-coding-governance-20260514-0520): Codex workspace agents and Claude cloud agents need repo rules: scoped boundary files, connector cards, and replay receipts reviewers can check.
- [Claude Code 2.1.141 team conventions](https://www.claudeworkshop.com/research/claude-team-conventions-20260514-0515): Claude Code 2.1.141 team conventions: a CLAUDE TOC, red-folder approvals, data-class tags on MCP connectors, and a weekly retro note.
- [Claude Code 2.1.140: team conventions](https://www.claudeworkshop.com/research/claude-team-conventions-20260513-0516): Claude Code 2.1.140 team conventions: a skill index for precedence, a hook budget, a CLAUDE TOC, and red-folder approvals reviewers can trace.
- [AI coding agents workflow guardrails for browser control](https://www.claudeworkshop.com/research/agentic-coding-governance-20260511-0518): Workflow guardrails for AI coding agents with browser control: child receipts, decision stubs, scope ledgers, and a supremacy clause reviewers can audit.
- [Agentic coding governance that holds in review](https://www.claudeworkshop.com/research/agentic-coding-governance-20260505-0449): Agentic coding governance as an operating guide: connector ownership, scope ledgers, decision stubs, and review receipts for MCP-connected engineering teams.
- [AI coding agents need workflow guardrails](https://www.claudeworkshop.com/research/agentic-coding-governance-20260503-0501): Workflow guardrails for AI coding agents: a precedence clause, a replay mandate, connector cards, and child receipts that keep forks explainable in review.
- [Always-on AI code review governance](https://www.claudeworkshop.com/research/agentic-coding-governance-20260502-1759): AI code review governance for always-on agents: receipts, scopes, and owners that answer why a file changed without replaying chat.
- [AI agent boundaries that hold under pressure](https://www.claudeworkshop.com/research/agentic-coding-governance-20260501-0514): A boundary-setting guide to AI agent boundaries: connector cards, scope ledgers, child receipts, and decision stubs that stop permission drift.
- [Claude Code 2.1.126: MCP, hooks, skills](https://www.claudeworkshop.com/research/claude-team-conventions-20260501-0508): Use Claude Code 2.1.126 with clear MCP ownership, a hook budget, a skill index, and a short weekly review that keeps team conventions current.
- [Eval platform governance for AI coding teams](https://www.claudeworkshop.com/research/agentic-coding-governance-20260429-1104): A governance memo on eval platform governance: receipts behind scores, scoped harness access, and owners that stop Goodhart drift.
- [Agent boundaries for teams running coding agents](https://www.claudeworkshop.com/research/agentic-coding-governance-20260428-0443): How to set agent boundaries for teams: connector ownership, written scopes, and review receipts that keep agent diffs explainable after the session ends.

## Optional
- [Full research index](https://www.claudeworkshop.com/llms-full.txt): one line per article with title, keyword, URL, updated