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Advanced Claude Code training for engineering organizations

Move from individual Claude Code usage to a standardized AI-native engineering workflow — agentic development, skills, MCP, subagents, hooks and the review standards that keep them safe.

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Target audience

For engineering orgs already using Claude Code

This is not an introduction. It assumes your engineers have Claude Code installed and are already getting value from it individually — and that this is now the problem.

Who it is for

  • Software engineers using Claude Code daily with self-taught habits.
  • Tech leads and staff engineers who need a standard the team can follow.
  • Engineering managers balancing speed against review quality.
  • Platform teams asked to make agentic tooling safe and supportable.

Prerequisites

  • Claude Code in use by at least part of the team
  • A real codebase we can work against during the sessions
  • Engineers comfortable in the terminal and in their own stack

Formats

The business problem

Forty engineers, forty private workflows

Individual Claude Code adoption produces real gains and no institutional capability. Nothing is shared, nothing is reviewable, and the org cannot tell whether output quality is holding.

Symptoms

  • Everyone has their own prompts and none are in the repo
  • No shared skills, subagents or MCP servers
  • No agreed position on tests for agent-written code
  • Review load has quietly moved, not reduced
  • Onboarding a new engineer transfers none of it

What the training changes

  • Team conventions committed alongside the code
  • Shared skills and subagents everyone can use
  • MCP servers wired to your real internal systems
  • Hooks enforcing the standards automatically
  • An explicit review bar for agent-assisted work

Curriculum

Taught against your codebase

Every module lands in your repository, not in a sample project. What the team builds during the sessions is what they keep using afterwards.

Track 01 — Agentic development

Working with an agent, not around one

How to decompose work an agent can actually finish, and how to stay in control of a long-running session.

  • Claude Code
  • Agentic development
  • Codebase workflows
  • Planning and verification
  • Testing agent-assisted changes
  • Debugging what the agent did

Engineers who direct the work rather than accept whatever comes back.

Track 02 — Standardization

Making it the team's workflow

The extension points that turn individual habits into shared, reviewable engineering practice.

  • Skills
  • MCP
  • Subagents
  • Hooks
  • Engineering standards
  • Review and CI integration

Move from individual Claude Code usage to a standardized AI-native engineering workflow.

Process

How the engagement runs

A working bootcamp. By the end of it there are commits, not slides.

  1. 01

    Codebase review

    Stack, CI, review culture, current usage.

  2. 02

    Baseline session

    How the team works today, made explicit.

  3. 03

    Build the standard

    Skills, subagents, MCP and hooks, written together.

  4. 04

    Committed to the repo

    Conventions and tooling land in your codebase.

  5. 05

    Follow-up review

    What held, what drifted, what to tighten.

Expected outcomes

What is in the repository afterwards

Committed artifacts

  • Project conventions the agent reads
  • Shared skills for recurring team tasks
  • Subagent definitions for review and research
  • MCP connections to internal systems
  • Hooks enforcing the agreed standards

Practice

  • An agreed review bar for agent-assisted changes
  • A testing position the team has actually debated
  • Onboarding that transfers the workflow to new hires

Related

Before and after

What actually changes

This track is bought to change how the team works, not how individuals feel. Stated per role, then for token spend, data handling and measurement — the three that decide whether the standard survives contact with a deadline.

  1. Software engineer

    Before

    Personal prompts kept locally, re-invented per task, shared with nobody.

    After

    Shared skills and subagents committed to the repo, invoked the same way by everyone.

  2. Tech lead

    Before

    Reviews agent-assisted PRs against an unwritten standard that shifts by reviewer.

    After

    Reviews against an agreed bar, with hooks catching the mechanical part before it reaches them.

  3. Engineering manager

    Before

    Cannot tell whether output speed came at the cost of review quality.

    After

    Has an explicit, written position on testing and review for agent-assisted work.

  4. Platform engineer

    Before

    Asked to make agentic tooling safe with no defined surface to actually secure.

    After

    MCP connections and hooks defined, versioned and owned like the rest of the platform.

  5. New joiner

    Before

    Inherits nothing, and learns the team’s AI habits by osmosis over months.

    After

    Clones the repo and gets the team’s workflow along with the code.

  6. Token economics

    Before

    Sessions run unbounded, whole files are re-sent for one-line questions, and context is rebuilt from scratch after every interruption.

    After

    Context discipline is part of the standard: what belongs in project conventions, what belongs in a skill, and what should never be re-sent at all.

  7. Data handling

    Before

    No agreed position on what source, secrets, customer data or logs may enter a session — it varies by engineer and by deadline.

    After

    Access scope and redaction rules written down, with hooks enforcing the mechanical parts so compliance does not depend on memory.

  8. Productivity

    Before

    Speed is judged by feel. The cost that moved into review and rework is invisible, so the team argues about whether it is faster at all.

    After

    Measured across the whole cycle — including review and rework — against a baseline taken before the bootcamp, not a vendor benchmark.

Before you ask

Claude Code training, answered plainly

Our engineers are already good at this. What is left to teach?

Usually not prompting — usually everything around it. Skills, subagents, MCP and hooks are where individual fluency turns into a team standard, and in most organizations those are either unused or used by one enthusiast. The gap is standardization, not skill.

Do you work in our actual repository?

That is the intent, and the training is much weaker without it. Access scope, branch policy and what may leave your network are agreed with your security team before we start. If a production repo is not possible, a representative internal one works.

Will this increase our review burden?

It is one of the things we make explicit rather than assume away. Agent-assisted changes shift where review effort goes, and a team without an agreed bar tends to discover that painfully. Setting that bar — and automating parts of it with hooks and CI — is part of the curriculum.

How does this relate to your team training?

Different audiences and different intent. The corporate training track covers Claude fundamentals and role workflows for mixed business teams; this track assumes daily Claude Code use and is about engineering standards. Larger organizations usually run both.

Can you help us build what we identify during the bootcamp?

Yes — that is the workflow implementation engagement. Teams frequently surface two or three internal workflows worth building properly while standardizing, and those become the first candidates.

Start here

Start a Claude Adoption Assessment

Tell us how your engineers use Claude Code today. We come back with where the standard is missing and what a bootcamp would target first.

Talk to an AI Expert

hello@focus20labs.com