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I get better output from Claude Code by adding fewer tools, not more. Piling on MCP servers rarely helps, but the right official marketplace plugins, CLI tools, and skills do. Start with /plugin and picks like typescript-lsp and security-guidance, then add Supabase CLI, Playwright, GitHub CLI, and the GSD framework. That stack handles code, deploys, research, and browser work on its own.

When I first found Claude Code, I tried to connect every MCP server I could find. Within a week, the agent felt slower and less decisive, and it often picked the wrong tool for the job. The fix was almost always a smaller, more careful toolset.

Alembic is the standard migration tool for SQLAlchemy projects. You run alembic init, point it at your SQLAlchemy models, and use alembic revision --autogenerate to produce migration scripts. Alembic then applies those scripts in order with alembic upgrade head. You get repeatable, reviewable schema changes that work the same way everywhere your app runs. The latest stable release is Alembic 1.18.4. It supports SQLAlchemy 2.0 (now at 2.0.48) and its modern typed APIs.

Claude Code Agent Teams is an experimental feature, live since v2.1.32. It lets you run 2-16 Claude Code sessions under one team lead. Each teammate gets its own context window and full tool access. They talk through a shared task list and direct peer-to-peer messages. You turn it on with one config change, then describe the team you want in plain language. Claude handles the spawning, the assignment, and the coordination. The feature shines on work you can split up: multi-file refactors, cross-layer feature builds, and research-and-review jobs. The catch is that it costs 3-7x more tokens than a single session, and it cannot resume a session.

The single most important file in any Claude Code project is CLAUDE.md - a persistent instruction set that loads every session and shapes how the agent reads, writes, and verifies code. But CLAUDE.md alone is not what separates productive setups from fragile ones. The real productivity stack in 2026 combines CLAUDE.md conventions with on-demand skills, deterministic hooks, and git worktree isolation for running 10-15 parallel sessions against a single repository. Each session is scoped to one task, operating in its own branch, turning a solo developer into a small engineering team .

You can build a fully local, sandboxed code interpreter agent. You pair Ollama (running a reasoning model such as Scout, the smallest Llama 4 variant , or DeepSeek R1) with a Docker container that runs the generated Python code. The agent sends a prompt to the local LLM, which writes Python. That code goes into a locked-down container with no network and strict limits. The output feeds back to the LLM so it can fix and retry. The whole loop runs on your machine with zero cloud calls.

Systemd timers should replace cron for nearly every scheduled task on modern Linux. They log to the journal, manage dependencies, and add random delays to avoid resource stampedes. They also catch up on runs missed during a reboot. The one reason to keep cron is legacy support on minimal systems without systemd. If your distro shipped in the last decade, you have everything to switch.

This guide covers the real problems with cron. It explains how systemd timers work and migrates several cron jobs step by step. It also covers the sandboxing and resource controls that make timers a better fit for production.