複数端末・複数AIツール（Cursor / Claude Code）を行き来してもコンテキストを失わない作業環境を作る A practical guide to designing a workflow that preserves context when switching between mu…

As AI-assisted development tools become central to everyday workflows, one persistent frustration stands out: every time you switch tools or devices, your context evaporates. This article tackles that problem head-on, offering a structured approach to maintaining continuity across Cursor, Claude Code, and multiple machines.

Cursor and Claude Code occupy different niches in the AI tooling landscape. Cursor, built on VSCode, excels as an in-editor assistant with deep codebase awareness. Claude Code, Anthropic's terminal-native agent, shines in agentic, multi-step tasks that span the command line. Many developers naturally want to use both, routing work to whichever tool fits the moment. The problem is that each tool maintains context within its own session boundaries — switch tools or close a terminal, and the shared mental model you had built with the AI is gone.

The core insight the author offers is deceptively simple: stop storing context inside AI tools and start storing it in the file system. By explicitly writing down current task state, in-progress decisions, and reasoning behind choices into plain Markdown files — then syncing those files via Git or cloud storage — you create a context layer that any tool on any device can read. This turns what was ephemeral session memory into durable, portable project knowledge.

This approach aligns neatly with conventions already supported by both tools. Cursor has long recognized `.cursorrules` as a project-level instruction file that shapes AI behavior from the first token. Claude Code similarly reads `CLAUDE.md` to orient itself before responding. By treating these files not as static configuration but as living documents updated throughout the project lifecycle, developers can essentially hand-off context between tools the same way engineers hand-off work in a team standup.

The author also advocates for templatizing prompts — storing commonly used context-injection prompts as reusable files rather than retyping them each session. This small habit compounds quickly on longer projects, cutting the ramp-up cost of every new AI session from minutes to seconds.

This kind of externalized context management is becoming a recognized discipline in the AI engineering community. Concepts like "memory banks" and "context files" are appearing in open-source productivity frameworks and internal tooling at companies building on top of LLM APIs. Anthropic and OpenAI are both investing in persistent memory features on the platform side, but those remain tied to individual accounts and tools — they don't solve the cross-tool problem the author describes. For now, file-system-based externalization is arguably the most portable and auditable solution available.

For anyone working on research, long-form writing, or multi-week engineering projects where the cost of re-explaining context is high, the methodology here is directly actionable. It doesn't require new tools or subscriptions — just a shift in the habit of treating context as a first-class artifact of the project, not a byproduct of any single session.