Agent Aware Cli

When tasked with building or improving a Command Line Interface (CLI), follow these architectural and design best practices to ensure it serves both human operators (with rich TUIs) and AI agents (with deterministic, machine-readable output).

Core Principles

1. The Dual-Experience Paradigm (DX & AX) This CLI must be designed as a “Dual-Mode” interface, treating both humans and AI agents as first-class users.

   • DX (Developer Experience): Ergonomics for human operators. Emphasizes discoverability, rich TUIs (e.g., Bubbletea, Lipgloss), semantic coloring, and intuitive aliases.
   • AX (Agent Experience): Ergonomics for autonomous AI agents. Emphasizes determinism, strict machine-readable output modes (e.g., --json), headless authentication, aggressive data scoping (limits/filtering), and mutative safety (--dry-run).
   • Environment Overrides: Respect NO_COLOR and [APP]_NO_TUI environment variables to automatically fall back to non-interactive, unstyled modes.

2. Headless-Friendly Authentication

   • Agents cannot complete interactive, browser-based OAuth flows. CLIs must either support headless auth methods (Service Accounts, API Keys via ENV vars, --token flags) OR immediately fail with a clear, machine-readable error instructing the human operator to authenticate manually first.

3. Sane Defaults & Aggressive Filtering

   • Never dump unpaginated or unfiltered lists to stdout. Default to sensible limits (e.g., top 10 items). Provide rich filtering flags (e.g., --status, --mine, --limit) so agents can surgically query exactly what they need without blowing out their token context.

4. Mutative Safety (--dry-run)

   • For commands that create, update, or delete external state, implement a --dry-run flag. This allows an autonomous agent to test its parameter mapping and validate the resulting payload locally before executing the destructive action.

5. Context Window Protection

   • Agents have limited context windows. Actively truncate massive text blobs, sort critical action items to the top, and mask raw credentials by default to prevent credential leakage.
   • Provide opt-in verbosity (e.g., --full or --verbose) for agents or humans who explicitly request unfiltered data.

6. Executable Proactive Error Hints

   • Don’t just report failures; provide the exact steps to fix them.
   • Inject copy-pasteable terminal commands into stderr (e.g., Hint: run 'app init' to create a database). This enables autonomous agents to parse the error and self-correct seamlessly.

7. Delegated State Management

   • Avoid maintaining long-running state loops within the CLI if orchestrating complex workflows.
   • Use a stateless task identifier pattern (--task <ID>, --ref <ID>). Pass the ID back to the backend service, which maintains the token context window and conversation history.

8. Externalized Agent Instructions

   • Add an AGENTS.md or GEMINI.md file at the repository root explaining the tool’s usage, workflows, and standards for AI developers.
   • Provide an agentskills.io compliant skills/ directory with SKILL.md files for complex subcommands.

9. API Discovery & Progressive Convenience

   • When building a CLI against a large Google/REST API, analyze its Discovery Document (e.g. https://youtube.googleapis.com/$discovery/rest?version=v3) to understand the exact, strict mappings of resources, methods, query parameters, and required OAuth scopes.
   • Consider auto-generating the base commands directly from the Discovery Document to guarantee 100% API coverage.
   • However, raw REST mappings are often un-ergonomic for humans. You must layer “Convenience Wrappers” or “Hints” on top of the raw generated commands. For example, if a user asks to “list videos for a user”, the CLI should intelligently abstract away the fact that it takes two API calls (one to resolve the handle to a Channel ID, and another to fetch the playlist) under a single channel videos @user command.

Implementation with Go (Cobra & Viper)

When implementing a CLI in Go, use Cobra for command routing and Viper for configuration management.

Cobra Practices

• Structured Discoverability: Prevent a “wall of text” in the root help output by using Cobra’s GroupID to categorize commands (e.g., Task Management, Information).
• The Three Pillars of Documentation: Populate these fields for every command:
  • Short: A 5-10 word summary starting with an action verb for quick scanning.
  • Long: Detailed explanation of what it does, why, and its differentiation.
  • Example: 3-5 concrete, copy-pasteable examples showing common flag combinations.
• Fail Fast Hooks: Validate configuration and connectivity in PersistentPreRun hooks before executing heavy logic.

Viper & Configuration Practices

• XDG Compliance: Follow the XDG Base Directory Specification for configuration files (e.g., ~/.config/app/config.yaml).
• Smart Fallbacks Priority: Use Viper to ensure values resolve in this consistent order: Command Line Flag > Environment Variable > Config File > Default Value
• Secrets Management: Never allow API keys to leak into shell history. Prefer environment variables over CLI flags for secrets.

Visual Information Design (Human Mode)

When optimizing the CLI for human readability, draw on Tufte-inspired principles to minimize cognitive load:

• Maximize Data-Ink Ratio: Minimize non-essential “ink” like decorative borders. Use whitespace and positioning as the primary tools for conveying hierarchy.
• Resilient UI Formatting: Always apply padding and width enforcement to raw strings before applying terminal styling (e.g., with lipgloss). Invisible ANSI escape codes can otherwise corrupt fmt.Printf table alignments.
• The ANSI Alignment Gotcha: When building TUI tables in Go, native tools like text/tabwriter or fmt.Printf will break alignment if you pass strings containing invisible ANSI color codes. You MUST pad strings to their fixed widths before applying color wrappers, or use a robust layout engine like lipgloss.Table.
• Semantic Color Tokens: Use specific functional colors rather than aesthetic ones:
  • Accent (Blue): Navigation landmarks, headers, group titles.
  • Command (Grey/Light Grey): Scan targets like command names and flags.
  • Pass (Green): Success states and completed tasks.
  • Warn (Orange/Yellow): Transient or pending states, warnings.
  • Fail (Red): Errors and rejected states.
  • Muted (Dark Grey): De-emphasis for metadata, types, or defaults.
  • ID (Teal/Mint): Unique identifiers.
• Color Degradation: Ensure colors degrade gracefully when piped to a file, when --json is invoked, or when NO_COLOR is present.

Mandatory Implementation Checklist for Agents

When generating or updating a CLI command, you MUST ensure all of the following are implemented on your first pass. Do not treat UI/UX as an afterthought:

• Dual-Experience (DX/AX): Does the command support a --json flag or respect NO_COLOR/[APP]_NO_TUI?
• Headless Auth: Can an agent run this command autonomously without hanging on a browser-based OAuth prompt?
• Data Scoping: If returning a list, are there sane default limits and filtering flags (e.g., --limit, --status)?
• Mutative Safety: If the command mutates state, does it include a --dry-run flag for agent safety?
• Error Hints: Do errors print actionable, copy-pasteable terminal commands to stderr?
• XDG Compliance: Is configuration handled via the XDG Base Directory specification (e.g. ~/.config/app/config.yaml) instead of local .env files?
• The Three Pillars: Are Short, Long, and Example fields fully populated in the Cobra command definition?
• Discoverability: Is the command assigned to a logical GroupID?
• Semantic Colors: Is the human-readable output formatted using the semantic color tokens (e.g., Lipgloss)?
• Alignment Safety: If using raw ANSI colors in tables, is string padding applied before the color codes to prevent alignment breaking?
• Help Formatting: Have you overridden Cobra’s default SetUsageTemplate to apply semantic colors to the help output itself (e.g., coloring headers blue)?