Terminal AI Coding Assistant Cheat Sheet and Command Security Guide

What Is a Terminal AI Coding Assistant and How Does It Change the CLI Workflow?

A terminal AI coding assistant is a development aid built around a large language model and designed to run in the command line. It can read the codebase, explain requirements, generate patches, execute tests, and complete the loop from requirement to verification through a combination of conversation and commands.

The main takeaway: these tools are best suited for delegating mechanical coding tasks to the model while leaving architecture decisions, boundary validation, and quality acceptance to developers. Their value comes from three main areas: reducing context switching, accelerating repetitive changes, and building verification steps into the workflow.

One-Page Cheat Sheet: Terminal AI Coding Assistant

The cheat sheet below uses Git and Aider as an example. Aider is a common terminal AI coding assistant. Other assistants can follow the same general model: in-session slash commands plus Git-based review. Commands may vary by version. Before running anything, confirm details with aider --help or /help.

Scenario	Command or Shortcut	Purpose	Usage Note
Install the assistant	python -m pip install aider-install && aider-install	Install the Aider command-line tool	Follow the official documentation and create a virtual environment first
Start a session	aider --read README.md	Start in the project root and load the README	Confirm the working tree is clean first
Specify a model	aider --model <model>	Switch models or specify an inference backend	Teams should standardize models and record versions
View help	aider --help / /help	Check commands and options for the current version	Commands may change between versions
Add context	/add src/app.py	Let the assistant read a specific file	Do not add .env, secrets, or production credentials
Remove context	/drop src/app.py	Remove irrelevant files	Reduce noisy edits and unintended changes
View changes	/diff or git diff	Review patches generated by the assistant	Manual review is required before committing
Run tests	/test pytest -q	Trigger project tests to validate results	Confirm the test command works first
Run scripts	/run npm test	Run build, test, or check scripts	Confirm high-risk commands one by one
Undo changes	/undo	Revert the assistant’s most recent change	Use Git branches or backups for complex rollbacks
Commit code	git add . && git commit -m "feat: ..."	Commit manually and preserve an audit record	Check the file scope and any sensitive information
End session	/quit or Ctrl+C	Exit the assistant session	Commit or save necessary results first
Interrupt a command	Ctrl+C	Stop a running command	Prevent long-running tasks from blocking the terminal
Refresh the terminal	Ctrl+L	Clear the screen and view the latest output	Does not change code or the repository
Shell completion	Tab	Complete paths, commands, or options	Depends on the current shell configuration

How Do You Start, End, and Resume a Session?

Before starting, check the repository state. Recommended commands:

git status
git switch -c feat/ai-helper-demo
aider --read README.md

git switch -c creates an isolated branch and prevents assistant changes from polluting the main branch. aider --read README.md helps the assistant understand the project documentation first. If the team wants to disable automatic commits, after confirming the current version supports it, use --no-auto-commits to keep commits under manual control.

When ending a session, review the changes first. Run /diff, pytest -q, or the project’s corresponding test command. After confirming everything is correct, commit:

git status
git diff
git add .
git commit -m "feat: initial ai-assisted change"

To resume context, start the assistant again from the repository root. You can recover the task background through .aider.chat.history.md, session logs, or version control commit messages. If historical files are involved, apply an ignore or archiving policy to avoid leaking internal information.

How Do You Help the Assistant Understand Project Context?

Helping the assistant “see” the right files is more effective than describing requirements repeatedly. Recommended context types:

• Entry files: such as README.md, pyproject.toml, package.json, and go.mod.
• Target files: source code, tests, and configuration files that need to be modified.
• Constraint files: coding standards, directory descriptions, AGENTS.md, CONTRIBUTING.md, or team-specific guidelines.

Use /add to add specific files and /drop to remove irrelevant ones. For large projects, do not add the entire repository at once. Add files by module instead. Example:

/add docs/architecture.md
/add src/service/order.py
Please add boundary condition tests for order.py without changing the public interface.

This reduces context drift and makes diffs easier to review.

How Can You Safely Generate, Modify, and Refactor Code?

Use a small-step workflow: give the assistant one verifiable goal at a time. Here is a complete example.

# 1. Confirm a clean working tree
git status
# 2. Create an experiment branch
git switch -c feat/add-order-tests
# 3. Start the assistant and load context
aider --read README.md
# 4. Add target files in the assistant session
/add src/service/order.py
/add tests/test_order.py
# 5. Describe the requirement in natural language
Please add unit tests for src/service/order.py covering a successful order, insufficient inventory, and a zero-amount order. Do not modify the public interface.
# 6. Review the assistant's changes
/diff
git diff
# 7. Run tests to validate
/test pytest -q tests/test_order.py
# 8. Commit after manual approval
git add src/service/order.py tests/test_order.py
git commit -m "test: add order service unit tests"

If the tests fail, do not let the assistant keep guessing. Paste the failure log back into the session and ask it to explain the cause and provide a minimal fix. If the change set is too large, roll it back first:

git restore src/service/order.py
git clean -fd tests/

git clean deletes untracked files. Confirm the paths before executing it.

How Do You Control Command Execution and Permissions?

A terminal assistant may execute shell commands, so the principle of least privilege must be applied. Recommended practices:

• Run the assistant only in trusted repositories and branches.
• Use .aiderignore and .gitignore to exclude .env, secrets, logs, and production configuration.
• Keep manual confirmation for deletion, deployment, database migration, package publishing, and similar high-risk operations.
• Prefer /diff, git diff, and patch previews. If the tool supports dry-run or change preview modes, enable them.
• Never paste production database credentials, cloud access tokens, or private keys into a session.

If stronger isolation is needed, run the assistant in a container, virtual machine, or read-only workspace, then merge the final changes back into a controlled repository.

How Do You Review, Roll Back, and Keep an Audit Trail?

The risk of AI-generated code is not that it is “wrong,” but that unreviewed code enters the main branch. Each change should pass four checkpoints:

1. Review the diff: confirm the change scope, boundary conditions, and exception handling.
2. Run tests: execute at least the tests and static checks for the affected modules.
3. Check logs: confirm command execution results, dependency changes, and new files.
4. Keep records: include the task, model, and verification method in the commit message.

Rollback strategies should be tied to version control:

git restore <file>
git reset --soft HEAD~1
git revert <commit>

Replace <file> and <commit> with real values. reset --soft is suitable for undoing a commit while keeping the changes. revert is suitable for safely rolling back a commit that has already been pushed. Do not force-push public branches without a backup.

Common Mistakes and How to Avoid Them

• Letting the assistant modify code in a dirty working tree: run git status first, and use git stash push -u if necessary.
• Asking the assistant to refactor the entire project at once: break the task into module-level steps and verify each one.
• Not providing entry files: load at least README.md, configuration files, and the target source files.
• Accepting all patches blindly: review the diff, tests, and dependency changes.
• Giving secrets to the model: use placeholders, environment variables, and ignore rules.
• Ignoring version differences: commands may change in new versions. Use --help as the source of truth.
• Having no rollback path: create a branch, take a backup, or commit a baseline first.

How Do You Choose a Terminal AI Coding Assistant?

Compare candidates across these dimensions:

Dimension	Selection Question	Recommendation
Model capability	Does it understand the current language, framework, and repository size?	Test it with real repository tasks
Context capability	Can it read multiple files, directories, and project descriptions?	Validate large repositories and cross-file changes
Permission model	Does it restrict file access and command execution?	Enable least privilege and manual confirmation
Auditability	Does it output diffs, logs, and commit records?	Integrate it with Git and CI
Security and compliance	Does it support private deployment, data isolation, or compliance policies?	Let the security team evaluate it
Team collaboration	Can it share rules, templates, and history?	Store the cheat sheet in the repository

How Do You Use Vibe Coding Without Losing Control?

Vibe Coding emphasizes expressing intent in natural language and letting AI handle most of the coding work. It lowers the barrier to expression, but it does not remove engineering standards. When code is mainly generated by models, code review, automated testing, version control, and continuous integration become more important, not less.

Limit Vibe Coding to low-risk tasks such as script completion, test generation, documentation cleanup, and log analysis. For core transactions, permissions, security, and database changes, keep human design and review.

How Do You Maintain a Team Cheat Sheet?

Treat the cheat sheet like code. Store it in the repository at docs/ai-cli-cheatsheet.md, and review and update it alongside the code. Teams can standardize:

• Branch naming: feat/*, fix/*, chore/*
• Commit messages: type(scope): summary
• Test commands: pytest -q, npm test, go test ./...
• Ignore rules: .env, .aider.chat.history.md, secrets, and certificates
• Review checklist: diff, tests, permissions, rollback, and license

The efficiency of terminal AI coding assistants comes from three things: actionable commands, a verifiable process, and auditable records. Put the cheat sheet into the repository, and make generation, review, verification, and commit part of the daily workflow. This improves speed while protecting quality and security.