Summary: This guide walks you through using Continue for workflow automation — from first install to production-ready coding workflows. Continue is free, open-source, and built to fit directly into your existing editor, making it one of the most practical AI coding tools a solopreneur can add to their stack today.
Continue Workflow Automation: Complete Guide
By the end of this guide, you'll have Continue installed, configured, and running inside your editor — automating repetitive coding tasks, generating boilerplate, and speeding up every phase of your development workflow. This isn't a product pitch. It's a working playbook built for developers who actually ship things.
What is Continue and Why Use It?
Continue is an open-source AI code assistant that plugs directly into VS Code and JetBrains IDEs. It's not a standalone SaaS tool you switch tabs to use — it lives inside your editor, where you're already working.
The core idea: instead of copying code into ChatGPT and pasting it back, Continue brings the AI conversation directly into your coding environment. You highlight a function, ask it to refactor, and it edits in place. You describe a feature in plain English, and it generates the scaffolding without leaving your file.
For solopreneurs, this matters for three specific reasons.
First, it's free. Not freemium-with-a-paywall free. Actually free, as in open-source MIT-licensed free. You bring your own model — whether that's a local model via Ollama, Claude 3.7, GPT-4o, or Gemini Pro — and Continue handles the interface. No per-seat pricing. No enterprise bloat sitting on top of a feature you use 20% of.
Second, it's deeply customizable. You can define your own slash commands, build custom workflows, and configure exactly which models handle which tasks. A solopreneur running a lean stack can tune Continue to match their exact workflow rather than adapting to someone else's.
Third, it keeps your context local. If you're working on client code or proprietary logic, running a local model through Continue means that code never hits an external API. That's real privacy, not a privacy policy.
The category is Coding & Development, but the practical use case stretches across every automation-adjacent task a developer-solopreneur does: generating tests, writing documentation, refactoring legacy code, scaffolding new features, and automating the tedious parts of shipping software.
Getting Started with Continue — Step by Step
Step 1: Install the Continue Extension
Open VS Code and navigate to the Extensions panel (Ctrl+Shift+X on Windows/Linux, Cmd+Shift+X on Mac). Search for "Continue" and install the official extension published by Continue Dev. If you use a JetBrains IDE, head to the JetBrains Marketplace and install from there. Once installed, you'll see a Continue icon appear in your sidebar — click it to open the chat panel.
Step 2: Connect Your First Model
Continue doesn't bundle a model — you connect one. Click the settings icon inside the Continue panel and open config.json. This is where you define which LLM powers your assistant. For the fastest start, add your OpenAI or Anthropic API key and point Continue at GPT-4o or Claude 3.7 Sonnet. If you want a fully local setup with no API costs, install Ollama separately, pull a model like codellama or deepseek-coder, and set your provider to ollama in config.json. You'll see the model selector update in the Continue panel immediately.
Step 3: Run Your First In-Editor Query
Open any code file you're actively working on. Highlight a function or code block, then press Cmd+L (Mac) or Ctrl+L (Windows) to send it to the Continue chat panel with context. Type a plain-English instruction: "Refactor this to use async/await" or "Add error handling to this function." Continue will stream a response directly in the chat, and you can accept the diff with one click. This is the core loop — highlight, instruct, apply.
Step 4: Create a Custom Slash Command
This is where Continue workflow automation gets real leverage. Open config.json and add a custom slash command under the slashCommands array. For example, create a /docstring command that automatically generates documentation for any highlighted function. You define the prompt template, the model to use, and the output behavior. Once saved, you can type /docstring in the chat panel and Continue runs the pre-built workflow instantly. The Continue documentation on custom commands covers the full config schema.
Step 5: Build a Multi-Step Workflow with Context
Continue supports adding multiple context providers — file trees, documentation URLs, terminal output, even GitHub issues. In config.json, enable context providers like @codebase (for full project indexing) or @docs (to pull in external documentation). Now when you're building a new feature, you can type @codebase explain the authentication flow and Continue scans your actual project rather than guessing. String this together with a slash command and you've built a repeatable workflow: scan codebase context → generate feature scaffold → apply diff. This is Continue workflow automation working at full capacity — automated, context-aware, editor-native.
Continue Tips and Best Practices
Use model routing for different task types. Don't use the same model for everything. Configure a fast, cheap model (like GPT-4o Mini or a local Mistral) for autocomplete and quick edits. Reserve a more capable model (Claude 3.7 or GPT-4o) for complex refactors and architecture questions. Continue lets you set this per-task in config.json. You'll cut costs significantly without sacrificing quality where it counts.
Index your codebase with @codebase before starting any feature. The @codebase context provider runs a local embedding of your project files. Without it, Continue is working blind. With it, every query has full project context — which means fewer hallucinated function names, fewer "the type doesn't exist" errors, and actually useful suggestions. Run the indexing once, and it updates incrementally as you edit.
Write slash commands for every repetitive prompt. If you've typed the same instruction more than three times — "write a unit test for this function," "add TypeScript types," "convert to a React component" — turn it into a slash command. This is the fastest way to get ROI from Continue. Your custom commands become a personal automation library that compounds over time.
Use the terminal context provider for debugging loops. When something breaks, add @terminal to your Continue query. It pulls your most recent terminal output directly into the chat context. Instead of copying stack traces manually, you type @terminal what's causing this error? and Continue sees the actual error output. Faster debugging, fewer context-switching interruptions.
Common Continue Use Cases
Automated code documentation. Solopreneurs rarely have time to write thorough docstrings and README sections. Configure a /docs slash command that takes any highlighted class or function and generates structured documentation. Run it before shipping a module. Never ship undocumented code again without slowing down.
Boilerplate scaffolding for new projects. Starting a new client project means the same folder structures, config files, and base components every time. Use Continue to generate project scaffolding from a plain-English description. "Scaffold a Next.js project with Tailwind, Prisma, and NextAuth" gets you a working file structure in under a minute.
Automated test generation. Writing tests is the task most solopreneurs skip when under deadline pressure. Set up a /test slash command that generates unit tests for any highlighted function using your preferred test framework. It won't write perfect tests every time, but it gives you a working first draft that takes 30 seconds to review rather than 15 minutes to write.
Refactoring legacy code from client projects. Inherited a messy codebase from a previous developer? Use Continue's @codebase context combined with specific refactor prompts to systematically clean up technical debt. Ask it to identify inconsistent patterns, convert callback-based functions to promises, or apply a consistent naming convention across files. This kind of work used to take days — Continue compresses it into hours.
API integration scaffolding. When you're integrating a third-party API, point the @docs context provider at the API's documentation URL and ask Continue to generate the integration code. It reads the actual docs and writes code that matches the real API spec, not a stale training-data approximation.
Troubleshooting Common Issues
Model not responding or timing out. This usually means your API key is misconfigured or the model endpoint is unreachable. Open config.json, double-check your API key has no extra whitespace, and confirm the model name matches exactly what your provider expects (e.g., gpt-4o not gpt4o). If using Ollama locally, verify the Ollama server is running with ollama serve in your terminal before opening VS Code.
@codebase not finding relevant files. If Continue's codebase search returns irrelevant or empty results, the index may be stale. Open the Continue panel, click the settings icon, and trigger a re-index. Also check that your config.json doesn't have overly aggressive ignore patterns that are excluding your source files. Large monorepos may need manual scoping to a subdirectory.
Diffs applying to the wrong location. When Continue applies a suggested edit and it lands in the wrong place, it's almost always because the highlighted context was ambiguous. Be more specific with your selections — highlight the complete function or block, not just a few lines. Also make sure you're running a recent version of the extension; the Continue GitHub releases page shows the latest stable build and changelog.
Slash commands not appearing in the panel. If custom slash commands you've added to config.json don't show up when you type /, the config file likely has a syntax error. Run your config.json through a JSON validator — a single missing comma breaks the entire file silently. The Continue panel also shows a config error indicator in the bottom bar when it can't parse the file.
Key Takeaways
- Continue is free and open-source, with no per-seat pricing — a genuinely solopreneur-friendly tool that avoids enterprise bloat entirely.
- You bring your own model: local via Ollama, or cloud via OpenAI, Anthropic, or Gemini, giving you full control over cost and privacy.
- Custom slash commands are the highest-leverage feature — they turn your most common prompts into one-word automation that runs inside your editor.
- The
@codebaseand@docscontext providers are what separate Continue from a basic chatbot — they make the AI actually aware of your specific project. - Continue workflow automation compounds over time: the more you invest in configuring custom commands and context, the faster you ship on every future project.
Frequently Asked Questions
Do I need to create an account to use Continue?
No. Continue is open-source software you install as an IDE extension. There's no Continue account, no login, and no data sent to Continue's servers. The only credentials you need are API keys for whichever LLM provider you choose to use — and if you run a local model through Ollama, you don't even need those.
How much does Continue actually cost to use?
The extension itself is free. Your actual cost is whatever you spend on model API calls — which is zero if you use a local model. If you use OpenAI or Anthropic, expect typical costs in the range of a few dollars per month for moderate use, depending on model and volume. Compared to paid coding assistants that charge $15–$30/month per seat, Continue workflow automation with a cloud model is still significantly cheaper.
How long does it take to get up and running?
A basic setup — extension installed, model connected, first query running — takes under 15 minutes. A properly configured setup with codebase indexing and custom slash commands takes an afternoon. The official quickstart guide is genuinely useful and gets you to a working state fast.
What's the best feature to learn first?
Start with the basic in-editor chat loop: highlight code, press Cmd+L, give an instruction, apply the diff. Once that feels natural — usually within an hour of use — move to custom slash commands. That's where Continue workflow automation shifts from "useful assistant" to "actual time-saver."
Where do I get help if something breaks?
The Continue Discord community is active and the core team responds regularly. The GitHub repository has a detailed issues tracker — search before posting, because most common problems are already documented with fixes. The official docs at docs.continue.dev cover configuration options thoroughly.
If you're building with a lean stack and want an AI coding assistant that actually fits your workflow — not one designed for a 50-person engineering team — Continue is the right call. It's free, editor-native, and endlessly configurable.
Explore the full tool profile and community reviews at Continue on Metatools. Looking to build a complete solopreneur development stack? Browse stacks to see how other founders are combining tools, compare Continue against alternatives to make sure it's the right fit, or submit a tool if you've found something worth sharing.