10 Ways a CLI Coding Agent Boosted My Productivity by 80%

Ever feel like you’re the last person not using the latest AI dev tools? I was there too, until I discovered a CLI-based coding agent that I can run right in my terminal.

One of the CLI-based coding agents I recently came across is Forgecode, and I have experimented a lot with this tool. To help you understand its capabilities in the terminal, I have jotted down 10 real-world ways I’ve been using Forgecode in enterprise development.

These coding agents integrate smoothly with existing workflows (no new IDEs to learn) and act as a “complete coding agent” that can “write, refactor, and optimize code based on specifications,” debug complex problems, generate test suites, document code, propose architectural improvements, and more. In short, missing out on this AI shell means missing out on a secret weapon in your dev toolkit.

1. Writing and optimizing code from specs

One of the first things I tried was having Forge turn a specification into working code. For example, I had a spec for a Python function to convert timestamps to ISO format. I opened my project repo in the terminal and ran something like:

$ forge -p "Write a Python function that converts a Unix timestamp to an ISO 8601 formatted string."

Within seconds, Forgecode analyzed the context (my project’s Python environment) and returned a complete function, even suggesting optimizations like using Python’s built-in datetime module instead of manual parsing.

In one case, I asked it to implement an email-sending handler from a written API spec. Forge not only generated the handler code, but also optimized it (batch-sending if multiple emails) on the fly. I didn’t have to explain the surrounding code – Forge read my project files and tailored its output accordingly. This workflow of “tell the agent what I need, get code” made feature prototyping absurdly fast. (In fact, Forge’s docs show similar behavior – it can “scaffold the necessary components” when adding features like a dark mode toggle.)

2. Debugging complex issues

Next, I handed Forge cryptic runtime errors and bugs. For example, one day my Node.js app threw a mysterious TypeError: Cannot read property 'map' of undefined. Instead of diving into code, I ran:

$ forge -p "Why am I getting 'TypeError: Cannot read property \"map\" of undefined' in my UserProfile component?"

Forgecode immediately scanned the code, pinpointed that my array variable was null, and suggested guard clauses around it. It walked me through the likely cause and fix, effectively doing initial bug triage. This matched the official Forge behavior – given an error like that, Forge “will analyze the error, suggest potential causes based on your code, and propose different solutions”. I was impressed how it highlighted a missing initialization in our Redux store (something I had overlooked).

In another incident, I pasted a multi-page Python stack trace into Forge and it quickly isolated which import was failing. For enterprise-scale projects with tangled dependencies, having an AI assistant sift through logs and point at the faulty module is a huge time-saver.

3. Generating test suites

Writing tests is vital but tedious. Forge has become my automated test engineer. Suppose I finish implementing a new function calculateShippingCost(order). I open the code file, inspect it, then ask Forge to write tests:

$ forge -p "Generate a set of Jest unit tests for the calculateShippingCost(order) function, covering edge cases."

Forge returns a comprehensive test suite covering normal inputs and failure cases (e.g. negative order values, missing fields). It even comments the tests with explanations. I can simply copy those into a calculateShippingCost.test.js file and run them. On another project, I had to boost test coverage quickly for an audit, so I pointed Forge at an untested utility class, and it autogenerates Mocha tests.

The productivity jump is real: instead of manually writing dozens of assertions, Forge does the heavy lifting. (Under the hood, it’s following the guidance that “unit tests should be included for all new functions,” as I set in my config.) Even better, the tests it generates are actually runnable – I’ve used Forge many times to catch regressions before even running them manually.

4. Code documentation and tech specs

I treat Forge like a technical writer too. After coding a tricky algorithm, I often ask Forge to produce docstrings or design docs. For example:

$ forge -p "Document the function calculateTax in detail, including its parameters, return values, and an example."

It replies with a clear docstring or markdown snippet I can drop into my code or README. In one case, I showed Forge a legacy YAML workflow and asked: “Explain what this CI/CD pipeline is doing step by step.” Forge parsed the config and output a human-readable summary of each job. It even created a spec for a REST API I was planning: “Create an OpenAPI description for an endpoint that takes {userId} and returns profile data.” The result was a boilerplate YAML spec which I refined.

Forge also summarized complex modules: pointing it at a class, it paraphrased the logic into plain English. This utility is priceless during code review or knowledge transfer – new team members get up to speed faster because I can run a quick “explain this file” prompt. (While not in official examples, this use of Forge naturally follows its “analyze project structure and explain flow” pattern.)

5. Architectural suggestions

Forge isn’t just for small tasks; I even use it for high-level design. When planning a new microservice, I ask it for architecture advice. For example, I prompted:

$ forge -p "Propose a scalable microservices architecture for an e-commerce order processing system."

Forge reviewed our codebase and existing services (it can see our folder structure) and suggested splitting order intake, payment, and shipping into separate containers. It recommended a message queue between order creation and fulfillment, and identified an appropriate database model. Even if not 100% production-ready, the suggestions give a solid starting point.

In one case, I described our data needs and asked, “What database schema fits a blog with users, posts, comments, and categories?” Forge produced a schema with tables and relationships – exactly as seen in its docs example of “design a database schema for a blog”. For system-wide questions (“Should we use SQL or NoSQL for this data?”), it weighs pros/cons based on our project. This kind of architectural brainstorming with an AI avoided weeks of indecision: I could iterate on high-level ideas via chat until I had a plan.

6. Code understanding

When jumping into an unfamiliar codebase, Forge is like a senior dev ready to answer questions. Early on I asked it to explain our authentication flow:

$ forge -p "Explain how the authentication system works in this codebase."

True to form, Forge parsed multiple files (middleware, user model, controllers) and described the end-to-end process: from login request to JWT creation, mentioning the key modules involved. This matches the behavior in Forge’s docs – “analyze your project’s structure” and provide a detailed explanation. It even highlighted where OAuth tokens were verified. On a large Java codebase, it outlined which classes handled database access vs. business logic, which was a huge help onboarding.

For one particularly gnarly service, I ran forge -p "Summarize the purpose of each endpoint in this Spring controller." and got back a neat list of endpoints and their functions. Basically, any time I or a colleague asks “What does this code do?” Forge often has a quick, accurate answer after scanning the context. It’s invaluable during architecture reviews or when deciphering a coworker’s pull request.

7. Feature implementation

Adding new features is where Forge shines. We often describe requirements in natural language and let it draft the skeleton. For instance, to add a dark mode toggle to our React app, I typed:

$ forge -p "Implement a dark mode toggle in our React application."

Forge responded with a plan: update the global stylesheet, add a toggle component, and even provided example JSX for the button and corresponding CSS variables. It suggested storing preference in localStorage – exactly what our team ended up using. I then instructed it, “Write the React component for the toggle,” and it gave clean code with propTypes and comments. This workflow mirrors Forge’s own example: when asked about a dark mode toggle, “Forge will suggest the best approach… and even scaffold the necessary components”.

In another case, we needed to add logging middleware to an Express server; I told Forge what we had, and it generated a logger.js file using morgan. It even explained how to integrate it. It’s like having a senior engineer draft the boilerplate for each new feature so I can focus on fine-tuning the logic.

8. Troubleshooting and debugging (environment issues)

Beyond code bugs, Forge has helped troubleshoot environment and deployment problems. For example, our CI pipeline suddenly started failing with a generic Docker permission error. I asked:

$ forge -p "Our Docker build is failing with a permission denied error when creating a directory. What could be wrong?"

Forge analyzed common causes and noticed that we were creating files as root in the container without setting correct ownership. It suggested using chown or running as a non-root user, exactly the fix needed. In another scenario, a colleague had trouble with environment variables not loading in production.

I described the .env setup to Forge, and it pointed out that our Dockerfile forgot to copy .env before npm install. By following Forge’s hint, we caught the misconfiguration quickly. Essentially, I treat the CLI as a first-pass troubleshooter for any issue – whether it’s a segmentation fault, a failing test, or a flaky Jenkins job, prompting Forge often surfaces the root cause or next steps. (This is the natural extension of its “debugging assistance” role into ops land.)

9. Refactoring legacy systems

Dealing with old, tangled code used to be daunting. Now I use Forge to modernize and refactor. For example, we had a decade-old PHP module that needed cleaning. I told Forge:

$ forge -p "Refactor this legacy function to improve readability and error handling."

It rewrote the function in a more modular way, adding try/catch and renaming variables for clarity. In another case (and matching its documentation example), I asked it to convert a class-based React component to hooks:

$ forge -p "Refactor the class-based UserProfile component to use React Hooks instead."

Forge walked through each part – moving state into useState, replacing lifecycle methods with useEffect – and gave the new functional component. The transformation was smooth and correct; I only needed minor edits afterward. This exactly follows the example from the docs, where Forge “can help modernize your codebase” by refactoring class components to hooks. Across the board, using Forge to do the grunt work of restructuring means I’m less scared of touching legacy code. I’m essentially “pair programming” with the AI to incrementally modernize our tech debt.

10. Git operations and history management

Finally, Forge has become a handy assistant for Git too. I’ll often prompt it to handle version control tasks. For instance, I needed to merge a stale feature branch with conflicts:

$ forge -p "Merge branch 'feature/login' into 'main' and help resolve any conflicts."

Forge scanned the diff and interactively suggested how to reconcile differences, even auto-editing conflict markers. In one real case, it noticed a key rename conflict and recommended keeping the latest schema change. This aligns with Forge’s own git example: given a merge with conflicts, “Forge can guide you through resolving git conflicts”.

I’ve also used it to generate conventional commit messages: by installing a small custom command, I can run /commit and Forge writes a semantic commit message for me (e.g. “feat(login): add remember-me checkbox”). For release management, I asked Forge to summarize commit history and it produced a changelog draft. Essentially, any time I’m doing branch juggling, blame analysis, or PR writing, Forge smooths the process. In fact, the documentation even shows custom Git commands in forge.yaml, like automating commit or PR generation. I can’t count how many hours I’ve saved by letting an AI prepare my git commands and messages.

And if you like what you see, ⭐ Star the GitHup repo to stay in the loop and support the project!

Ready to supercharge your workflow?

I was skeptical at first, but Forgecode’s CLI assistant has become my most-used dev tool. By integrating right into my terminal (no context switch to a web UI), it feels like a natural extension of my dev environment. And it’s just a few steps to get started:

Here's the GitHub Repo:Forgecode

Install it via NPM (npm i -g @antinomyhq/forge), set your FORGE_KEY from forgecode.dev, and run forge. That’s it.

Forge is now ready to assist you with your development tasks.

If you care about shipping code faster and smarter, give Forge CLI a try – your future commits (and your team) will thank you!

Conclusion

CLI tools like Forge are quietly reshaping how enterprise teams build software. They keep developers in the terminal. With Forge, routine and even complex tasks (from coding to docs to CI) become faster and less error-prone. There’s nothing mystical about it. it’s just a smart agent that plugs into your shell and leverages your context. And because it runs locally with your own API keys, it stays secure and private.

If you haven’t tried it yet, I encourage you to give Forge a spin. Install the CLI, connect your model of choice, and start asking it to “fix this bug” or “generate tests.” You may find that adding this AI assistant to your team accelerates development velocity more than you expected. After all, the future of enterprise development is collaborative and agentic – don’t miss out on how Forge can help your team build smarter and faster.

Let me know your experience!!!