A Personal AI Workflow Stack: Claude Code CLI, CC Switch, and 9router
The stack solves two concrete pain points for developers using LLMs from the command line: managing API costs and configuration across reinstalls, and keeping model access reliable when a primary provider's quota runs out or its network is unreachable. The 9router Combo pattern, which chains models into automatic fallback sequences, is a lightweight alternative to paid multi-provider routing services.
The core setup runs on Node.js with Claude Code CLI as the primary engine, chosen for its direct file-system access and project-level context awareness. CC Switch handles API usage monitoring and one-click configuration backups, while 9router acts as a local model gateway that can chain multiple providers into fallback Combos, automatically switching when a primary model hits a quota limit or errors out. The workflow also incorporates scenario-based Skill loading inside Claude Code and a weekly review habit to refine prompts and tool configurations.
For developers inside China's network environment, the guide addresses the practical friction of API access by routing requests through 9router's built-in free providers or relay channels, though it warns against sending sensitive company code through third-party endpoints. An alternative tool, OpenCode, is noted for its domestic-network-friendly desktop client and interoperability with Claude's ecosystem.
The end state is a terminal-first routine: start 9router, launch Claude Code, load Skills by task type, and review costs monthly through CC Switch. The emphasis is on reducing tool-switching friction so attention stays on the problem at hand.
The workflow treats model access as an infrastructure reliability problem rather than a model-quality problem: 9router's Combo fallback chain is essentially a load-balancing and circuit-breaker pattern applied to LLM APIs.
CC Switch and 9router together separate two concerns that most AI coding tools bundle opaquely — cost governance and model routing — giving the developer explicit control over both without leaving the terminal.
The recommendation to load Skills per scenario rather than globally is a small but meaningful performance constraint: it acknowledges that prompt bloat from unused extensions directly slows inference, which matters more in a CLI where latency is felt immediately.
The guide's security warning about third-party model relays reflects a real tension in regions where official API access requires a proxy: the convenience of free or relayed endpoints trades off against the risk of leaking proprietary code to unvetted servers.
The discussion splits between a demand for more advanced, original content and a defense of introductory material. The pushback argues that basic tool tutorials still serve a large audience of newcomers who have only heard about AI but never used it. A reply promises a follow-up with higher-level AI usage.
I thought it would be about simplifying your own work by designing AI workflows, didn't expect it to just be a tutorial on a few tools [awkward grin]
Basic-level stuff,,,, [blushing]
Some of you guys don't roast me, not everyone is proficient with AI. This is a basic-level tutorial. There are still plenty of people like my coworkers who've only heard of it but never tried it [sweating]