AI Native CLI

Agent-Friendly CLI Spec v0.1

When building or modifying CLI tools, follow these rules to make them safe and reliable for AI agents to use.

Overview

A comprehensive design specification for building AI-native CLI tools. It defines 98 rules across three certification levels (Agent-Friendly, Agent-Ready, Agent-Native) with prioritized requirements (P0/P1/P2). The spec covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, self-description, and a feedback loop via a built-in issue system.

When to Use This Skill

• Use when building a new CLI tool that AI agents will invoke
• Use when retrofitting an existing CLI to be agent-friendly
• Use when designing command-line interfaces for automation pipelines
• Use when auditing a CLI tool's compliance with agent-safety standards

Core Philosophy

1. Agent-first -- default output is JSON; human-friendly is opt-in via --human
2. Agent is untrusted -- validate all input at the same level as a public API
3. Fail-Closed -- when validation logic itself errors, deny by default
4. Verifiable -- every rule is written so it can be automatically checked

Layer Model

This spec uses two orthogonal axes:

• Layer answers rollout scope: core, recommended, ecosystem
• Priority answers severity: P0, P1, P2

Use layers for migration and certification:

• core -- execution contract: JSON, errors, exit codes, stdout/stderr, safety
• recommended -- better machine UX: self-description, explicit modes, richer schemas
• ecosystem -- agent-native integration: agent/, skills, issue, inline context

Certification maps to layers:

• Agent-Friendly -- all core rules pass
• Agent-Ready -- all core + recommended rules pass
• Agent-Native -- all layers pass

How It Works

Step 1: Output Mode

Default is agent mode (JSON). Explicit flags to switch:

$ mycli list              # default = JSON output (agent mode)
$ mycli list --human      # human-friendly: colored, tables, formatted
$ mycli list --agent      # explicit agent mode (override config if needed)

• Default (no flag) -- JSON to stdout. Agent never needs to add a flag.
• --human -- human-friendly format (colors, tables, progress bars)
• --agent -- explicit JSON mode (useful when env/config overrides default)

Step 2: agent/ Directory Convention

Every CLI tool MUST have an agent/ directory at its project root. This is the tool's identity and behavior contract for AI agents.

agent/
  brief.md          # One paragraph: who am I, what can I do
  rules/            # Behavior constraints (auto-registered)
    trigger.md      # When should an agent use this tool
    workflow.md     # Step-by-step usage flow
    writeback.md    # How to write feedback back
  skills/           # Extended capabilities (auto-registered)
    getting-started.md

Step 3: Four Levels of Self-Description

1. --brief (business card, injected into agent config)
2. Every Command Response (always-on context: data + rules + skills + issue)
3. --help (full self-description: brief + commands + rules + skills + issue)
4. skills <name> (on-demand deep dive into a specific skill)

Certification Requirements

Each level includes all rules from the previous level. Priority tag [P0]=agent breaks without it, [P1]=agent works but poorly, [P2]=nice to have.

Level 1: Agent-Friendly (core -- 20 rules)

Goal: CLI is a stable, callable API. Agent can invoke, parse, and handle errors.

Output -- default is JSON, stable schema

• [P0] O1: Default output is JSON. No --json flag needed
• [P0] O2: JSON MUST pass jq . validation
• [P0] O3: JSON schema MUST NOT change within same version

Error -- structured, to stderr, never interactive

• [P0] E1: Errors -> {"error":true, "code":"...", "message":"...", "suggestion":"..."} to stderr
• [P0] E4: Error has machine-readable code (e.g. MISSING_REQUIRED)
• [P0] E5: Error has human-readable message
• [P0] E7: On error, NEVER enter interactive mode -- exit immediately
• [P0] E8: Error codes are API contracts -- MUST NOT rename across versions

Exit Code -- predictable failure signals

• [P0] X3: Parameter/usage errors MUST exit 2
• [P0] X9: Failures MUST exit non-zero -- never exit 0 then report error in stdout

Composability -- clean pipe semantics

• [P0] C1: stdout is for data ONLY
• [P0] C2: logs, progress, warnings go to stderr ONLY

Input -- fail fast on bad input

• [P1] I4: Missing required param -> structured error, never interactive prompt
• [P1] I5: Type mismatch -> exit 2 + structured error

Safety -- protect against agent mistakes

• [P1] S1: Destructive ops require --yes confirmation
• [P1] S4: Reject ../../ path traversal, control chars

Guardrails -- runtime input protection

• [P1] G1: Unknown flags rejected with exit 2
• [P1] G2: Detect API key / token patterns in args, reject execution
• [P1] G3: Reject sensitive file paths (*.env, *.key, *.pem)
• [P1] G8: Reject shell metacharacters in arguments (; | && $())

Level 2: Agent-Ready (+ recommended -- 59 rules)

Goal: CLI is self-describing, well-named, and pipe-friendly. Agent discovers capabilities and chains commands without trial and error.

Self-Description -- agent discovers what CLI can do

• [P1] D1: --help outputs structured JSON with commands[]
• [P1] D3: Schema has required fields (help, commands)
• [P1] D4: All parameters have type declarations
• [P1] D7: Parameters annotated as required/optional
• [P1] D9: Every command has a description
• [P1] D11: --help outputs JSON with help, rules, skills, commands
• [P1] D15: --brief outputs agent/brief.md content
• [P1] D16: Default JSON (agent mode), --human for human-friendly
• [P2] D2/D5/D6/D8/D10: per-command help, enums, defaults, output schema, version

Input -- unambiguous calling convention

• [P1] I1: All flags use --long-name format
• [P1] I2: No positional argument ambiguity
• [P2] I3/I6/I7: --json-input, boolean --no-X, array params

Error

• [P1] E6: Error includes suggestion field
• [P2] E2/E3: errors to stderr, error JSON valid

Safety

• [P1] S8: --sanitize flag for external input
• [P2] S2/S3/S5/S6/S7: default deny, --dry-run, no auto-update, destructive marking

Exit Code

• [P1] X1: 0 = success
• [P2] X2/X4-X8: 1=general, 10=auth, 11=permission, 20=not-found, 30=conflict

Composability

• [P1] C6: No interactive prompts in pipe mode
• [P2] C3/C4/C5/C7: pipe-friendly, --quiet, pipe chain, idempotency

Naming -- predictable flag conventions

• [P1] N4: Reserved flags (--agent, --human, --brief, --help, --version, --yes, --dry-run, --quiet, --fields)
• [P2] N1/N2/N3/N5/N6: consistent naming, kebab-case, max 3 levels, --version semver

Guardrails

• [P1] I8/I9: no implicit state, non-interactive auth
• [P1] G6/G9: precondition checks, fail-closed
• [P2] G4/G5/G7: permission levels, PII redaction, batch limits

Reserved Flags

Level 3: Agent-Native (+ ecosystem -- 19 rules)

Goal: CLI has identity, behavior contract, skill system, and feedback loop. Agent can learn the tool, extend its use, and report problems -- full closed-loop collaboration.

Agent Directory -- tool identity and behavior contract

• [P1] D12: agent/brief.md exists
• [P1] D13: agent/rules/ has trigger.md, workflow.md, writeback.md
• [P1] D17: agent/rules/*.md have YAML frontmatter (name, description)
• [P1] D18: agent/skills/*.md have YAML frontmatter (name, description)
• [P2] D14: agent/skills/ directory + skills subcommand

Response Structure -- inline context on every call

• [P1] R1: Every response includes rules[] (full content from agent/rules/)
• [P1] R2: Every response includes skills[] (name + description + command)
• [P1] R3: Every response includes issue (feedback guide)

Meta -- project-level integration

• [P2] M1: AGENTS.md at project root
• [P2] M2: Optional MCP tool schema export
• [P2] M3: CHANGELOG.md marks breaking changes

Feedback -- built-in issue system

• [P2] F1: issue subcommand (create/list/show)
• [P2] F2: Structured submission with version/context/exit_code
• [P2] F3: Categories: bug / requirement / suggestion / bad-output
• [P2] F4: Issues stored locally, no external service dependency
• [P2] F5: issue list / issue show <id> queryable
• [P2] F6: Issues have status tracking (open/in-progress/resolved/closed)
• [P2] F7: Issue JSON has all required fields (id, type, status, message, created_at, updated_at)
• [P2] F8: All issues have status field

Examples

Example 1: JSON Output (Agent Mode)

$ mycli list
{"result": [{"id": 1, "title": "Buy milk", "status": "todo"}], "rules": [...], "skills": [...], "issue": "..."}

Example 2: Structured Error

{
  "error": true,
  "code": "AUTH_EXPIRED",
  "message": "Access token expired 2 hours ago",
  "suggestion": "Run 'mycli auth refresh' to get a new token"
}

Example 3: Exit Code Table

0   success         10  auth failed       20  resource not found
1   general error   11  permission denied 30  conflict/precondition
2   param/usage error

Quick Implementation Checklist

Implement by layer -- each phase gets you the next certification level.

Phase 1: Agent-Friendly (core)

1. Default output is JSON -- no --json flag needed
2. Error handler: { error, code, message, suggestion } to stderr
3. Exit codes: 0 success, 2 param error, 1 general
4. stdout = data only, stderr = logs only
5. Missing param -> structured error (never interactive)
6. --yes guard on destructive operations
7. Guardrails: reject secrets, path traversal, shell metacharacters

Phase 2: Agent-Ready (+ recommended) 8. --help returns structured JSON (help, commands[], rules[], skills[]) 9. --brief reads and outputs agent/brief.md content 10. --human flag switches to human-friendly format 11. Reserved flags: --agent, --version, --dry-run, --quiet, --fields 12. Exit codes: 20 not found, 30 conflict, 10 auth, 11 permission

Phase 3: Agent-Native (+ ecosystem) 13. Create agent/ directory: brief.md, rules/trigger.md, rules/workflow.md, rules/writeback.md 14. Every command response appends: rules[] + skills[] + issue 15. skills subcommand: list all / show one with full content 16. issue subcommand for feedback (create/list/show/close/transition) 17. AGENTS.md at project root

Best Practices

• Do: Default to JSON output so agents never need to add flags
• Do: Include suggestion field in every error response
• Do: Use the three-level certification model for incremental adoption
• Do: Keep agent/brief.md to one paragraph for token efficiency
• Don't: Enter interactive mode on errors -- always exit immediately
• Don't: Change JSON schema or error codes within the same version
• Don't: Put logs or progress info on stdout -- use stderr only
• Don't: Accept unknown flags silently -- reject with exit code 2

Common Pitfalls

• Problem: CLI outputs human-readable text by default, breaking agent parsing Solution: Make JSON the default output format; add --human flag for human-friendly mode

• Problem: Errors reported in stdout with exit code 0 Solution: Always exit non-zero on failure and write structured error JSON to stderr

• Problem: CLI prompts for missing input interactively Solution: Return structured error with suggestion field and exit immediately

Related Skills

• @cli-best-practices - General CLI design patterns (this skill focuses specifically on AI agent compatibility)

Additional Resources

• Agent CLI Spec Repository

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags ai-native-cli <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns