Routines is Claude Code's cloud automation system (formerly Cloud Scheduled Tasks). Beyond cron scheduling, you can trigger runs via API endpoint or GitHub events — scan issues, review PRs, run checks, open PRs — all while your computer is off.
Claude Code only reads CLAUDE.md; Codex only reads AGENTS.md. Teams using both end up maintaining two identical files. Fix: make CLAUDE.md a symlink pointing to AGENTS.md — one source of truth.
Biome does the work of ESLint + Prettier in a single tool, running 10–20x faster with far less configuration. DaoDao uses it across an entire monorepo — lint and format in one pass.
Global skills live in ~/.claude/skills/, but they go missing in new sessions or the Desktop App? The problem usually isn't a missing file — it's that the skill descriptions aren't being loaded into context. This post clarifies the CLI vs Desktop App differences, the role of settings.json, and the most reliable fix.
Use OpenSpec to break requirements into engineering tasks, Claude Code to implement them, hooks to auto-format and protect, local review before committing, three AI reviewers running in parallel on PR, and auto-deploy after merge. This entire workflow lets one person maintain quality across six sub-projects.
Hooks are Claude Code's event system. They trigger shell commands, HTTP requests, or LLM evaluations automatically before/after tool execution, when a prompt is submitted, or when a task ends. Use them to block dangerous operations, run automated reviews, inject context, or write audit logs.
A Skill is an SOP written for AI. Define the steps in a Markdown file and Claude follows them. No coding required, no frameworks to learn — just write down what an experienced person would do.
Stuck mid-debug and can't fix it right now? Use /file-bug-issue to package the error analysis, reproduction steps, and attempted fixes from your conversation into a well-structured GitHub issue. Pair it with a Remote Agent to let AI automatically take over the fix.
Using Claude Code's Scheduled Remote Agent, automatically scan GitHub issues every 2 hours, implement features, open PRs, and address review feedback — no human intervention required. Humans only write issues and click merge. Pair it with the custom /publish-tasks skill to push OpenSpec engineering tasks directly to GitHub issues.
Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.
Three main classification systems dominate: Conventional Comments (label-based), Google's severity prefixes (Nit/Optional/FYI), and SonarQube's four quadrants (Bug/Vulnerability/Code Smell/Hotspot). AI review tools have each developed their own taxonomies, but the core dimensions consistently converge on four areas: correctness, security, performance, and maintainability.