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Building Clis

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ancoleman
building-clis

Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.

Overview

Publisherancoleman
Repositoryai-design-components
Skill namebuilding-clis
Stars
523
Forks
73
Bundled files
19
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ancoleman on GitHub. Read the source before you install it.

Installation

Install the Building Clis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ancoleman/ai-design-components.git /tmp/ai-design-components
mkdir -p .claude/skills
cp -r /tmp/ai-design-components/skills/building-clis .claude/skills/building-clis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Building Clis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Building Clis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Building Clis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Building CLIs

Build professional command-line interfaces across Python, Go, and Rust using modern frameworks with robust argument parsing, configuration management, and shell integration.

When to Use This Skill

Use this skill when:

  • Building developer tooling or automation CLIs
  • Creating infrastructure management tools (deployment, monitoring)
  • Implementing API client command-line tools
  • Adding CLI capabilities to existing projects
  • Packaging utilities for distribution (PyPI, Homebrew, binary releases)

Common triggers: "create a CLI tool", "build a command-line interface", "add CLI arguments", "parse command-line options", "generate shell completions"

Framework Selection

Quick Decision Guide

Python Projects:

  • Typer (recommended): Modern type-safe CLIs with minimal boilerplate
  • Click: Mature, flexible CLIs for complex command hierarchies

Go Projects:

  • Cobra (recommended): Industry standard for enterprise tools (Kubernetes, Docker, GitHub CLI)
  • urfave/cli: Lightweight alternative for simple CLIs

Rust Projects:

  • clap v4 (recommended): Type-safe with derive API or builder API for runtime flexibility

For detailed framework comparison and selection criteria, see references/framework-selection.md.

Core Patterns

Arguments vs. Options vs. Flags

Positional Arguments:

  • Primary input, identified by position
  • Use for required inputs (max 2-3 arguments)
  • Example: convert input.jpg output.png

Options:

  • Named parameters with values
  • Use for configuration and optional inputs
  • Example: --output file.txt, --config app.yaml

Flags:

  • Boolean options (presence = true)
  • Use for switches and toggles
  • Example: --verbose, --dry-run, --force

Decision Matrix:

Use CaseTypeExample
Primary required inputPositional Argumentgit commit -m "message"
Optional configurationOption--config app.yaml
Boolean settingFlag--verbose, --force
Multiple valuesVariadic Argumentfiles...

See references/argument-patterns.md for comprehensive parsing patterns.

Subcommand Organization

Flat Structure (1 Level):

app command1 [args]
app command2 [args]

Use for: Small CLIs with 5-10 operations

Grouped Structure (2 Levels):

app group subcommand [args]

Use for: Medium CLIs with logical groupings (10-30 commands) Example: kubectl get pods, kubectl create deployment

Nested Structure (3+ Levels):

app group subgroup command [args]

Use for: Large CLIs with deep hierarchies (30+ commands) Example: gcloud compute instances create

See references/subcommand-design.md for structuring strategies.

Configuration Management

Standard Precedence (Highest to Lowest):

  1. CLI Arguments/Flags (explicit user input)
  2. Environment Variables (session overrides)
  3. Config File - Local (./config.yaml)
  4. Config File - User (~/.config/app/config.yaml)
  5. Config File - System (/etc/app/config.yaml)
  6. Built-in Defaults (hardcoded)

Best Practices:

  • Document precedence in --help
  • Validate config files before execution
  • Provide --print-config to show effective configuration
  • Use XDG Base Directory (~/.config/app/) for config files

See references/configuration-management.md for implementation patterns across languages.

Output Formatting

Format Selection:

Use CaseFormatWhen
Human consumptionColored text, tablesDefault interactive mode
Machine consumptionJSON, YAML--output json, piping
Logging/debuggingPlain text--verbose, stderr
Progress trackingProgress bars, spinnersLong operations

Best Practices:

  • Default to human-readable output
  • Provide --output flag (json, yaml, table)
  • Use stderr for logs, stdout for data
  • Auto-detect TTY (disable colors if not interactive)
  • Use exit codes: 0 = success, 1 = error, 2 = usage error

See references/output-formatting.md for formatting strategies.

Language-Specific Quick Starts

Python with Typer

Installation:

bash
pip install "typer[all]"  # Includes rich for colored output

Basic Example:

python
import typer
from typing import Annotated

app = typer.Typer()

@app.command()
def greet(
    name: Annotated[str, typer.Argument(help="Name to greet")],
    formal: Annotated[bool, typer.Option(help="Use formal greeting")] = False
):
    """Greet someone with a message."""
    greeting = "Good day" if formal else "Hello"
    typer.echo(f"{greeting}, {name}!")

if __name__ == "__main__":
    app()

Key Features:

  • Type hints for automatic validation
  • Minimal boilerplate with decorators
  • Auto-generated help text
  • Rich integration for colored output

See examples/python/ for complete working examples including subcommands, config management, and interactive features.

Go with Cobra

Installation:

bash
go get -u github.com/spf13/cobra@latest

Basic Example:

go
var rootCmd = &cobra.Command{
    Use:   "greet [name]",
    Args:  cobra.ExactArgs(1),
    Run: func(cmd *cobra.Command, args []string) {
        fmt.Printf("Hello, %s!\n", args[0])
    },
}

rootCmd.Flags().Bool("formal", false, "Use formal greeting")
rootCmd.Execute()

Key Features:

  • POSIX-compliant flags
  • Viper integration for configuration
  • Subcommand architecture
  • Shell completion generation

See examples/go/ for complete working examples including Viper config and multi-level subcommands.

Rust with clap

Installation (Cargo.toml):

toml
[dependencies]
clap = { version = "4.5", features = ["derive"] }

Basic Example (Derive API):

rust
use clap::Parser;

#[derive(Parser)]
#[command(about = "Greet someone")]
struct Cli {
    /// Name to greet
    name: String,

    /// Use formal greeting
    #[arg(long)]
    formal: bool,
}

fn main() {
    let cli = Cli::parse();
    let greeting = if cli.formal { "Good day" } else { "Hello" };
    println!("{}, {}!", greeting, cli.name);
}

Key Features:

  • Compile-time type safety
  • Derive API (declarative) or Builder API (programmatic)
  • Comprehensive validation
  • Performance optimized

See examples/rust/ for complete working examples including subcommands and builder API patterns.

Interactive Features

Progress Indicators

Python (rich):

python
from rich.progress import track
for _ in track(range(100), description="Processing..."):
    time.sleep(0.01)

Go (progressbar):

go
import "github.com/schollz/progressbar/v3"
bar := progressbar.Default(100)
for i := 0; i < 100; i++ {
    bar.Add(1)
}

Rust (indicatif):

rust
use indicatif::ProgressBar;
let bar = ProgressBar::new(100);
for _ in 0..100 {
    bar.inc(1);
}

Prompts and Confirmations

Python:

python
confirm = typer.confirm("Are you sure?")
if not confirm:
    raise typer.Abort()

Go:

go
reader := bufio.NewReader(os.Stdin)
fmt.Print("Are you sure? (y/n): ")
response, _ := reader.ReadString('\n')

Rust:

rust
use dialoguer::Confirm;
if Confirm::new().with_prompt("Are you sure?").interact()? {
    // Proceed
}

Shell Completion

Generating Completions

Python (Typer):

bash
_MYAPP_COMPLETE=bash_source myapp > ~/.myapp-complete.bash
_MYAPP_COMPLETE=zsh_source myapp > ~/.myapp-complete.zsh

Go (Cobra):

go
rootCmd.AddCommand(&cobra.Command{
    Use:   "completion [bash|zsh|fish|powershell]",
    Args:  cobra.ExactArgs(1),
    Run: func(cmd *cobra.Command, args []string) {
        switch args[0] {
        case "bash":
            rootCmd.GenBashCompletion(os.Stdout)
        case "zsh":
            rootCmd.GenZshCompletion(os.Stdout)
        }
    },
})

Rust (clap):

rust
use clap_complete::{generate, shells::Bash};
generate(Bash, &mut Cli::command(), "myapp", &mut io::stdout())

See references/shell-completion.md for installation instructions.

Distribution and Packaging

Python (PyPI)

pyproject.toml:

toml
[project]
name = "myapp"
version = "1.0.0"
scripts = { myapp = "myapp.cli:app" }

Publish:

bash
pip install build twine
python -m build
twine upload dist/*

Go (Homebrew)

Formula:

ruby
class Myapp < Formula
  desc "My CLI application"
  url "https://github.com/user/myapp/archive/v1.0.0.tar.gz"

  def install
    system "go", "build", "-o", bin/"myapp"
  end
end

Rust (Cargo)

Publish:

bash
cargo login
cargo publish

Installation:

bash
cargo install myapp

See references/distribution.md for comprehensive packaging strategies including binary releases.

Best Practices

Universal CLI Conventions

Always Provide:

  • --help and -h for usage information
  • --version and -V for version display
  • Clear error messages with actionable suggestions

Argument Handling:

  • Use -- separator for options vs. positional args
  • Support both short (-v) and long (--verbose) forms
  • Validate and sanitize all user inputs

Error Handling:

  • Exit code 0 for success
  • Exit code 1 for general errors
  • Exit code 2 for usage errors
  • Write errors to stderr, data to stdout

Interactivity:

  • Detect TTY (interactive vs. piped input)
  • Provide --yes/--force to skip prompts for automation
  • Show progress for operations longer than 2 seconds

Configuration Best Practices

File Formats:

  • Use YAML, TOML, or JSON consistently
  • Separate files per environment (dev, staging, prod)
  • Validate configuration in CI/CD with --check-config

Secret Management:

  • Never commit secrets to config files
  • Use environment variables or secret managers
  • Document required environment variables

Precedence:

  • CLI args > env vars > config file > defaults
  • Document precedence in help text
  • Provide --print-config to show effective configuration

Integration with Other Skills

testing-strategies:

building-ci-pipelines:

api-patterns:

  • Building API client CLIs
  • Authentication and token management
  • Formatting API responses

secret-management:

  • Secure credential storage
  • Environment variable integration
  • Vault/secrets manager integration

Reference Files

Decision Frameworks:

Implementation Guides:

Code Examples:

Quick Reference

Framework Recommendations:

  • Python: Typer (modern) or Click (mature)
  • Go: Cobra (enterprise) or urfave/cli (simple)
  • Rust: clap v4 (derive or builder)

Common Patterns:

  • Arguments: Primary inputs (max 2-3)
  • Options: Named parameters with values
  • Flags: Boolean switches
  • Subcommands: Group related operations
  • Config: CLI args > env vars > files > defaults

Output Standards:

  • Default: Human-readable (colored, tables)
  • Machine: JSON/YAML via --output flag
  • Errors: stderr, data: stdout
  • Exit: 0 = success, 1 = error, 2 = usage

Distribution:

  • Python: PyPI (pip install)
  • Go: Homebrew, binary releases
  • Rust: Cargo (cargo install), binary releases

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Building Clis AI skill do?

Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.

Why use Building Clis on TypingMind?

Because you install it once and use it with any model. Building Clis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Building Clis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ancoleman/ai-design-components/tree/main/skills/building-clis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Building Clis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Building Clis?

As many as you like. As long as a model supports skills, you can use Building Clis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Building Clis AI skill free?

Yes. It is published on GitHub by ancoleman under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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