Terminal AI Coding Assistants: 10 Core Commands and Best Practices

Throughout this article, ai-cli stands in for whatever command-line AI coding assistant you are using. Specific subcommand names and parameters vary slightly across vendors and versions, so always defer to the actual output of ai-cli --help on your machine. The focus here is on the design intent behind these capabilities and the methodology for using them.

1. Why the terminal is the main battlefield for these tools

Command-line AI coding assistants became popular not because of a clean interface, but because they naturally occupy three positions:

1. Tightly bound to the working directory: where the process is launched largely determines the scope of its code indexing, file search, and dependency analysis. Pick the wrong directory and every subsequent question is invalid.
2. Sharing the same origin as the permission model: the files it can read or write and the commands it can run are equivalent to your current user’s permissions. This makes the “confirmation step” the only real security gate.
3. Scriptable and orchestrable: terminal programs have standard input/output, exit codes, and pipe support by nature, so they can be embedded into CI, code-review bots, and batch-refactoring scripts—something a GUI struggles to do.

From this follows the most common newcomer mistake: treating the terminal assistant as a chatbot. It is actually a process with read/write permissions. The correct way to use it is: “define its working scope → let it build understanding → confirm outputs one by one.”

2. Ten core commands (grouped by stage)

Group 1: Entry and session (these decide your starting quality)

1. Launch the session in the project root

The most basic step, and the most easily overlooked. The launch directory effectively defines the “context boundary”: the tool usually scans config files, directory structure, and dependency manifests centered on it. A typical consequence of launching in the wrong directory is that the tool can’t see your build scripts and has to guess your tech stack.

cd /path/to/your/repo   # enter the repository root, not a subdirectory or home directory
ai-cli

Practical advice: always launch from the version-control root; when you have multiple repos side by side, open a separate session for each rather than hopping across projects in one session.

2. Launch with a specific model tier

Most terminal assistants let you switch models via a launch parameter or an in-session command. The industry convention is to offer several reasoning-strength tiers, essentially a trade-off within the “quality–latency–cost” triangle:

Tier	Typical characteristics	Applicable scenarios
Lightweight	Fastest response, lowest unit cost	Formatting, comment generation, small single-file edits, renaming refactors
Standard	Balanced quality and speed	Everyday business-code development, unit-test writing, routine debugging
High-reasoning	Strongest deep-analysis ability, highest cost and latency	Architecture design, complex bug localization, cross-module refactors, code review

ai-cli --model <tier-identifier>      # specify at launch

Key practice: don’t stay on the highest tier the whole time. A very effective cost-saving technique is “plan with the high-reasoning tier, execute with the standard tier”: first have the strong model produce a refactoring plan and a file list, then switch to the standard tier to implement against that list.

3. Resume a previous session

Two capabilities that are often confused:

• Resume a specific session: list past sessions for you to choose from, useful for returning to a particular project or discussion.
• Continue the most recent one directly: skip the picker and pick up the latest session, ideal for resuming immediately after an interruption.

ai-cli --resume        # interactively choose a historical session
ai-cli --continue      # directly resume the most recent session (short flag in some implementations)

The value is preserving your decision trail. Code context you’ve already explained, options you’ve rejected, and naming conventions you’ve agreed on are all in the session memory—no need to repeat them. Use cases: being pulled away mid-change by a meeting, refactors completed across multiple days, and bug hunts that need continuous tracking.

Conversely, session resumption is also the main source of context pollution. Cram three unrelated tasks into one session and it only gets worse the more you resume. The rule: one task, one session.

Group 2: Self-inspection (these lower the learning cost)

4. View help and version

ai-cli --help
ai-cli --version

The help output is the highest-information-density documentation, and hidden capabilities almost always live there: non-interactive mode, output format, permission flags, log levels, context-management switches, and so on. Build the habit: re-read --help after every version upgrade—many new features appear only in the flag list and never make it into any announcement.

5. In-session self-inspection and context-usage checks

Inside a session there is usually a set of instant slash-prefixed commands (/help to list available commands, status commands to check the current model/directory/usage, and usage commands to inspect context occupancy). These solve the “why did it suddenly get dumber” attribution problem.

A practical rule of thumb: as context usage approaches the window limit, the model’s attention is diluted by historical noise, and its behavior degrades—answering off-topic, repeating finished work, forgetting earlier constraints. When you see occupancy climbing too high, it’s time to split the task, not keep asking.

Group 3: Project context (these set the ceiling on answer quality)

6. Initialize project context

This has the highest return on investment among all ten commands, and it’s also the step newcomers skip most often.

The idea: let the tool scan the repo structure, build config, dependency declarations, and test framework, and generate a project-level instruction file (an industry-wide convention is emerging across tools, e.g., an AGENTS.md-style file in the root; some tools use their own filename). This file is read automatically at the start of every session—effectively freezing a “project manual” into place.

ai-cli /init

After generation, review and trim it by hand; don’t commit it as-is. A high-quality instruction file should include:

# Project conventions (example skeleton)
## Tech stack and versions
- Runtime version, package manager (pin these; never write "latest")
## Directory responsibilities
- What src/xxx is responsible for, and what should not go where
## Build and verification commands
- The exact commands for compiling, formatting, static analysis, and unit tests
## Coding standards
- Naming conventions, error-handling approach, logging rules, forbidden dependencies
## Delivery requirements
- Which checks must run after a change; commit-message format

A useful rule of thumb: the more specific the instruction file, the lower the rework rate. “Use a reasonably recent dependency” is worse than stating the exact version; “pay attention to quality” is worse than giving a runnable check command.

7. Clear context and compress context

Two operations with entirely different meanings that you must distinguish:

• Clear: completely discard the current session memory and start from zero. Used when switching to an unrelated new task.
• Compress: distill a long session’s history into a summary and continue, keeping conclusions and dropping the process. Used when you’ve been on the same task for a long time but aren’t done.

/clear      # reset context; the model no longer remembers anything from before
/compact    # summarized continuation that keeps task conclusions (supported by some implementations)

The decision criterion is clear: if the task changed, clear; if the task is the same but the session got too long, compress. The typical symptoms of choosing wrong are—after clearing, the model loses all project conventions and you have to re-explain the context; or after compressing, it’s still distracted by tens of thousands of lines of irrelevant diff.

Group 4: Control and integration (from “using it” to “engineering with it”)

8. Permission and allowlist policy

Since a terminal assistant can read/write files and run commands, permission management is not optional. Common practices include setting a permission mode per session (confirm each step by default, allow certain tools without confirmation, or fully open), and maintaining an allowlist in a config file (e.g., allow read-only commands like git status and ls, while keeping confirmation for deletes, force-pushes, and network requests).

ai-cli --permission-mode <mode-identifier>   # exact values per --help

Three security bottom lines: be lenient with read-only commands, but always confirm writes and deletes; work under version control so that git diff becomes your last review net; never paste secrets, production database connection strings, or real user data into the session.

9. Non-interactive batch processing

This is the dividing line between “toy usage” and “production usage.” Most tools support a one-shot prompt-execution mode: pass in a task description, get the result printed directly, and exit without entering the interactive UI.

# read the task description from a file, write results to a file
ai-cli --print "Add documentation comments to all exported functions under src/" > report.md
# pipe content in (stdin) to compose with the shell
git diff --cached | ai-cli --print "Review this change along these points: correctness, edge cases, and security; output JSON"

Because it has exit codes and standard output, it can plug into pre-commit hooks, CI pipelines, and batch-migration scripts. When you run batch jobs as scheduled tasks in container platforms or function-compute/serverless environments (for example, using related capabilities from a cloud provider such as Baidu AI Cloud) and collect execution records with logging and monitoring/alerting services, you get a traceable automation checkpoint.

10. Structured output and multi-turn orchestration

For batch processing to be truly usable, the output must be machine-parseable. Request JSON/JSONL or CSV, then use jq or a Python script to extract fields and apply thresholds:

git diff --cached \
  | ai-cli --print --output-format json "Review the change; output an array of {severity, file, line, issue, suggestion}" \
  | jq -e '[.[] | select(.severity=="high")] | length == 0' \
  || { echo "High-severity issues found; blocking commit"; exit 1; }

The advanced move is pipeline orchestration: have a small model do mechanical bulk extraction, hand the aggregated results to a strong model for an overall judgment, and let a script decide whether to allow or block. This combination of “tiered scheduling + a structured contract” is what it actually takes to embed an AI assistant into the development process.

3. Chaining commands into a workflow: a reusable paradigm

# 1) correct directory + appropriate tier
cd ~/work/my-service && ai-cli --model standard
# 2) first time in the project: build context (review the output before committing)
> /init
# 3) focus on a local area to avoid broad scanning
> Only look at src/order/ and its test directory
# 4) advance the implementation, verifying after each step
> Compile and run unit tests; if they fail, fix and rerun
# 5) reset immediately when switching tasks
> /clear
# 6) resume when you need to reuse conclusions
$ ai-cli --continue

A three-level context strategy is worth remembering: the project-level instruction file handles “long-term, stable project conventions,” directory focus handles “the current working scope,” and session memory handles “temporary decisions for this task.” Separating these responsibilities is what keeps them from contaminating each other.

4. Common anti-patterns

• Launching in the wrong directory, so the tool can’t see the build config and has to guess;
• Staying on the top-tier model throughout, using the strongest reasoning even for formatting, losing on both cost and latency;
• One session spanning multiple tasks, drifting further off-target the longer you go, until the only option is to /clear and start over;
• Never initializing project context, forcing you to re-explain the tech stack and verification commands every time;
• Blindly accepting all changes without reviewing line by line with git diff, pushing the risk to production;
• Using it as a search engine, asking general-knowledge questions beyond the repo, which wastes context and returns inaccurate results.

5. Conclusion

The essence of these ten capabilities isn’t memorizing commands; it’s three things: defining working boundaries (directory and permissions), managing cognitive load (context initialization, compression, and clearing), and turning interaction into process (batch processing and structured output). Master the three main lines first—entry, resumption, and project context—then plug batch processing into your own pre-commit checks and CI, and your efficiency will change by an order of magnitude. As for the exact spelling of each subcommand, just check --help whenever you need to—it’s the one reference that’s always accurate.