Introducing cctk: the Claude Code Toolkit
A community-vetted collection of slash commands, subagents, hooks, and MCP integrations. One install, sensible defaults, deeply customizable.
Deep dives on workflows, patterns, and case studies. Written by developers who ship with Claude Code every day.
The unglamorous but high-value use case that gets ignored in most Claude Code content — using it to understand code you didn't write.
The most popular framing for AI coding tools compares them to a smart intern. Where the analogy holds, where it doesn't, and what to use instead.
Not every hook that seemed valuable stayed valuable. The five we retired, the specific failure modes that killed them, and the meta-lessons.
A subagent prompt is code, and code needs tests. Six patterns for testing subagents in ways that catch prompt regressions.
I joined the team three months ago with 200+ commands, subagents, hooks, and skills. What worked, what didn't, and what teams should do differently.
Enabling prompt caching is easy. Getting meaningful savings from it is where most teams stall. Four specific patterns we've validated in production.
A community-vetted collection of slash commands, subagents, hooks, and MCP integrations. One install, sensible defaults, deeply customizable.
The framing that fixed how we write, maintain, and iterate on our CLAUDE.md. Treat it like documentation and it decays. Treat it like a product with users, feedback loops, and outcomes, and it stays useful.
For six months we treated subagents like config files: commit them to git, done. Then a "small tweak" broke twelve engineers' workflows silently for a week. This is the version control discipline we adopted after that.
One subagent produces output. A second subagent reviews it before shipping. This isn't just belt-and-suspenders — the second reader catches a specific class of failure the primary can't catch on its own.
The first month your team's Anthropic bill hits four figures, someone will ask "where is this coming from?" The answer is less obvious than you'd think. Here's the actual anatomy of an AI-assisted-work bill.
Context windows have grown to 200K+ tokens. That looks like abundance. In practice, it isn't — attention degrades before the window fills, and every token loaded competes for that attention.
The moment your slash command's definition contains "if the user wants X, do Y; otherwise do Z," you've outgrown the primitive. Branching logic in slash commands is one of those anti-patterns that seems reasonable until you've watched it fail.
The rule works about 80% of the time. The other 20% is where subagent design gets interesting. Here's when to follow the rule, when to combine, when to split further, and how to tell which situation you're in.
Every team building with agentic loops has watched one spiral — 40 minutes, $30 in tokens, confident wrong output. The fix isn't model choice or better tools. It's prompt structure. Here's the three-part structure that made our loops boring.
For a year, our team used Claude Code with ad-hoc prompts — each engineer building their own. It worked until it didn't. This is the story of migrating to a shared subagent library: the trigger, the extraction methodology, the mistakes, and where we landed.
The three primitives look similar in the docs. In practice they solve different problems, and picking the wrong one produces surprisingly bad outcomes. A decision tree that our team actually uses, plus six scenarios walked through.
Every session pays the token tax. Every engineer scrolls past most of it. Every update is a merge conflict waiting to happen. Here's why big CLAUDE.md files are worse than useless and what to do with the content instead.
The default MCP server design pattern is "expose every capability of the underlying service." It's the wrong pattern. Focused MCPs beat comprehensive MCPs in almost every deployment. Here's why, and how to build the focused version.
The workflows that turn Claude Code from "cool AI tool" into "how our team actually gets work done" — with the specific commands, agents, and hooks that make each one work.
Six months of measured data on pr-reviewer subagent adoption. What worked, what didn't, and the honest limits of AI-assisted code review.
Claude Code hooks are the most underrated part of the platform. These twelve turn team standards from "we should really enforce that" into invariants that get enforced automatically.
We had a working custom Sentry integration for Claude Code. We rewrote it as an MCP server. Here's the honest tradeoff analysis and what we learned six months later.
A dozen devs, one migration, and the parts nobody warned us about. Written from the middle, not the end.
The most valuable file in your repo is also the easiest to make worse. Concrete before/after examples from real teams.
Not every task needs Opus. Not every task can survive Haiku. A framework and specific numbers from three months of production routing.
A short story about a `DROP TABLE`, a lifecycle hook, and the thin line between an AI agent that's helpful and one that's dangerous.
A regulated-environment deployment story. What worked, what didn't, and what surprised our compliance team more than us.
Three primitives that overlap enough to confuse teams. A decision framework and the specific rules we use to pick.
A checklist for evaluating MCP servers before they touch your production data — permissions, provenance, network scope, and the questions our team asks every time.
How to run Claude Code from CI to review PRs, backfill tests, sweep docs, and catch regressions — with workflow YAML and hard-earned lessons on cost, quotas, and idempotency.
How to structure CLAUDE.md files so context stays useful across a large codebase — hierarchical layouts, scoped instructions, and the anti-patterns that make big monorepos worse.
The design rules we settled on after building thirty-plus subagents in production — scope, prompts, tool selection, and the failure modes that shaped every rule.
A specific breakdown of where our tokens were going, what we changed, and the six optimizations that produced most of the savings — without asking anyone to use Claude less.
How to build a Claude Skill for a real domain workflow — the design, the file layout, the mistakes we made, and what to check before you ship one to your team.
Six categories of work where we've stopped reaching for Claude Code, why, and what we use instead. An honest counter to "AI-first everything" thinking that's hurt more than it helped.
What actually changed when we started using Claude Code hands-free during commutes, walks, and cooking. The workflows that work, the ones that don't, and the interaction patterns nobody warned us about.
A staged rollout over four months across a 60-engineer team. The onboarding path that stuck, the resistance patterns we didn't anticipate, and the metrics we actually watched.
Most AI productivity metrics are performative. Here's the smaller set that actually predicts whether Claude Code is helping your team — and why the popular ones don't.
Financial, healthcare, and government workflows have constraints that most Claude Code content ignores. The audit trails, data boundaries, and approval patterns that make it work in regulated environments.
The specific ways Claude Code changed our on-call rotation — from initial page to postmortem — with the commands, subagents, and human checkpoints that make it survive real production stress.
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