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Agent Cli

Community
glebis
agent-cli

Add agent-friendly --json NDJSON output to Python CLI scripts, or scaffold a complete cli_utils package for a project. Use this skill when the user wants to make scripts machine-readable for AI agents, add --json flags, convert print statements to structured JSON, build a CLI helper library, create an open-source CLI-for-agents package, add structured logging, or make CLI output machine-readable. Also use when the user mentions NDJSON, structured CLI output, agent-friendly CLI, non-interactive scripts, or JSON I/O for automation.

Overview

Publisherglebis
Repositoryclaude-skills
Skill nameagent-cli
Stars
379
Forks
56
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Agent Cli 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/glebis/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/agent-cli .claude/skills/agent-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Cli 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 Agent Cli 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 Agent Cli 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.

Agent-Friendly CLI Builder

Convert Python CLI scripts from human-only output to agent-consumable NDJSON, or scaffold a complete cli_utils package ready for open-source distribution.

Two Modes

Mode A: Convert an existing script

When the user points at a script and says "make this agent-friendly" or "add --json":

  1. Scan the script for output points
  2. Generate cli_utils.py if the project doesn't have one
  3. Replace all output with structured helpers
  4. Verify no raw output leaks in JSON mode

Mode B: Scaffold a complete package

When the user says "create a cli_utils package" or wants an open-source library:

  1. Scaffold a full Python package with pyproject.toml, tests, license, README
  2. Include all helpers: json_log, json_error, die, log, add_json_flag, enable_json, is_json
  3. Add pytest test suite with full coverage
  4. Add MIT license (or ask user preference)

Core Architecture

The fundamental pattern: every script gets a --json flag. When active, all stdout becomes newline-delimited JSON (NDJSON). Each line is a self-contained JSON object with a standard envelope.

The NDJSON Event Envelope

Every JSON line has at minimum:

json
{"event": "ready", "ts": "2026-04-30T14:00:00+00:00", "pid": 1234, "port": 8765}
  • event — what happened (snake_case string)
  • ts — ISO 8601 UTC timestamp
  • Additional fields are event-specific kwargs

Why This Design

  • NDJSON over JSON arrays: processable line-by-line, one bad line doesn't break the stream, works with grep/jq, low memory for long-running processes
  • --json opt-in over default: preserves human DX, doesn't break existing scripts or habits
  • Global mode flag over per-call checks: set once at startup, every helper respects it automatically
  • die() over repeated if/else: the pattern if is_json(): json_error(); sys.exit(1) else: print(); sys.exit() appears constantly — die() collapses it to one line

The cli_utils.py Reference Implementation

When generating cli_utils.py, produce exactly this (adapt only if the project has specific needs):

python
"""Shared helpers for JSON CLI output."""
import json
import os
import sys
from datetime import datetime, timezone

_json_mode = False

def enable_json():
    global _json_mode
    _json_mode = True

def is_json():
    return _json_mode

def json_log(event: str, **kwargs):
    """Emit one NDJSON line to stdout."""
    obj = {"event": event, "ts": datetime.now(timezone.utc).isoformat(), **kwargs}
    print(json.dumps(obj, default=str), flush=True)

def json_error(message: str, **kwargs):
    """Emit a structured error event."""
    json_log("error", message=message, **kwargs)

def die(message: str, code: int = 1, **kwargs):
    """Print error and exit — JSON or human depending on mode."""
    if _json_mode:
        json_error(message, **kwargs)
    else:
        print(message, file=sys.stderr)
    sys.exit(code)

def add_json_flag(parser):
    """Add --json flag to an argparse parser."""
    parser.add_argument("--json", action="store_true",
                        help="NDJSON output for agent consumption")

def log(message: str, **json_kwargs):
    """Print human message normally, or emit JSON event if --json is active."""
    if _json_mode:
        json_log(json_kwargs.pop("event", "info"), message=message, **json_kwargs)
    else:
        print(message)

def json_ready(**kwargs):
    """Emit the readiness signal — only in JSON mode. Call early in daemon startup."""
    if _json_mode:
        json_log("ready", pid=os.getpid(), **kwargs)

Converting a Script — Step by Step

Step 1: Scan for output points

Search the target script for all places that produce output or exit:

bash
grep -n 'print(\|sys\.exit\|exit(\|input(\|os\.system.*say' TARGET.py

Categorize each hit:

  • Informational print → replace with log(message, event="descriptive_name")
  • Error + exit → replace with die(message)
  • Status line with \r → replace with if is_json(): json_log("status", ...) else: print("\r...", end="", flush=True)
  • Interactive input() → guard with if not is_json(): or add --no-interactive flag
  • Side effects (say, osascript, notifications) → guard with if not is_json():
  • Import-time errors (before argparse runs) → use sys.exit("message") (writes to stderr)

Step 2: Add the import and flag

At the top of the script, after existing imports:

python
from cli_utils import add_json_flag, enable_json, is_json, json_log, log, die

In the if __name__ == "__main__" block, add to argparse:

python
add_json_flag(parser)
args = parser.parse_args()
if args.json:
    enable_json()

Step 3: Replace each output point

Apply the categorization from Step 1. Key patterns:

Simple informational:

python
# Before
print(f"Connected to {device}")

# After
log(f"Connected to {device}", event="connected", device=device)

Error + exit:

python
# Before
print("Device not found")
sys.exit(1)

# After
die("Device not found")

Daemon readiness (first output after initialization):

python
# Before
print(f"Server running on port {port}")

# After — json_ready() only emits in JSON mode, so always call it + human fallback
json_ready(port=port)
log(f"Server running on port {port}", event="ready", port=port)

Status lines (\r overwrite):

python
# Before
print(f"\r  HR {hr} RMSSD {rmssd:.1f}", end="", flush=True)

# After
if is_json():
    json_log("status", hr=hr, rmssd=rmssd)
else:
    print(f"\r  HR {hr} RMSSD {rmssd:.1f}", end="", flush=True)

Human-only output (banners, usage examples):

python
if not is_json():
    print("Usage: send {\"type\": \"join\", \"name\": \"Alice\"}")

Step 4: Verify

  1. Run python script.py --help — confirm --json flag appears
  2. Run python script.py --json — confirm first line is valid JSON
  3. Grep for remaining raw print( calls — ensure each is guarded or intentional

Event Name Conventions

Use snake_case, be descriptive, keep them grep-friendly:

CategoryEvents
Lifecycleready, shutdown, connected, disconnected
Datahr, status, metric, heartbeat
Errorserror, retry
Actionsrecording_started, recording_stopped, preset_change
Progressscanning, connecting, downloading, importing

Scaffolding an Open-Source Package

When the user wants a distributable package, scaffold this structure:

cli-utils-agent/
├── pyproject.toml
├── LICENSE                  # MIT by default, ask user
├── README.md
├── src/
│   └── cli_utils_agent/
│       ├── __init__.py      # re-exports all public API
│       └── core.py          # the implementation
├── tests/
│   ├── __init__.py
│   ├── test_json_log.py
│   ├── test_die.py
│   ├── test_log.py
│   └── test_add_json_flag.py
└── .github/
    └── workflows/
        └── test.yml         # CI with pytest

__init__.py — re-export public API

python
# src/cli_utils_agent/__init__.py
from .core import (
    enable_json, is_json, json_log, json_error, die,
    add_json_flag, log, json_ready,
)

__all__ = [
    "enable_json", "is_json", "json_log", "json_error", "die",
    "add_json_flag", "log", "json_ready",
]

README.md template

Generate a README with: project name, one-line description, install instructions (pip install cli-utils-agent), quick usage example showing add_json_flag + enable_json + log(), API reference table listing all exports with one-line descriptions, and a link to the research background.

pyproject.toml template

toml
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "cli-utils-agent"
version = "0.1.0"
description = "Add agent-friendly --json NDJSON output to any Python CLI"
readme = "README.md"
license = "MIT"
requires-python = ">=3.10"
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Topic :: Software Development :: Libraries",
]

[project.urls]
Homepage = "https://github.com/USER/cli-utils-agent"

[tool.hatch.build.targets.wheel]
packages = ["src/cli_utils_agent"]

Test suite

Use capsys for stdout capture, pytest.raises(SystemExit) for die(). Example:

python
# tests/test_json_log.py
import json
from cli_utils_agent import json_log, enable_json, is_json

def test_json_log_writes_ndjson(capsys):
    json_log("ready", port=8765, pid=42)
    line = capsys.readouterr().out.strip()
    obj = json.loads(line)
    assert obj["event"] == "ready"
    assert obj["port"] == 8765
    assert "ts" in obj

# tests/test_die.py
import json
import pytest
from cli_utils_agent import die, enable_json
from cli_utils_agent import core as _core

def test_die_human_mode(capsys):
    _core._json_mode = False
    with pytest.raises(SystemExit) as exc:
        die("something broke")
    assert exc.value.code == 1
    assert "something broke" in capsys.readouterr().err

def test_die_json_mode(capsys):
    _core._json_mode = True
    try:
        with pytest.raises(SystemExit):
            die("something broke", code=10)
        obj = json.loads(capsys.readouterr().out.strip())
        assert obj["event"] == "error"
        assert obj["message"] == "something broke"
    finally:
        _core._json_mode = False

Cover: json_log, json_error, die, log, add_json_flag, enable_json/is_json, json_ready.

GitHub Actions CI

yaml
# .github/workflows/test.yml
name: Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.10", "3.11", "3.12", "3.13"]
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - run: pip install -e ".[dev]"
      - run: pytest -v

Add dev dependencies to pyproject.toml:

toml
[project.optional-dependencies]
dev = ["pytest>=8.0"]

Checklist — Run Before Declaring Done

After converting a script or creating a package:

  • --help shows --json flag
  • Running with --json produces valid NDJSON (every line is parseable JSON)
  • First JSON line from daemons has "event": "ready"
  • Error paths emit "event": "error" with non-zero exit code
  • No raw print() can fire when --json is active
  • Import-time errors (missing deps) use sys.exit("message") not print()
  • Interactive prompts are guarded
  • Side effects (voice, notifications) are guarded
  • Human output is preserved when --json is NOT passed
  • Unhandled exceptions don't leak tracebacks to stdout in JSON mode (wrap main in try/except, emit json_error)
  • Tests pass (if package mode)
  • Package builds cleanly: python -m build (if package mode)
  • Install in clean venv and import works (if package mode)

Further Reading

Read references/best-practices.md when you need:

  • Heartbeat patterns for liveness detection (section 2.4)
  • Exit code conventions and string error codes (section 2.3)
  • Schema introspection with --schema (section 2.5)
  • CLI vs MCP decision matrix (section 2.7)
  • Token efficiency tips for agent consumption (section 5)

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 Agent Cli AI skill do?

Add agent-friendly --json NDJSON output to Python CLI scripts, or scaffold a complete cli_utils package for a project. Use this skill when the user wants to make scripts machine-readable for AI agents, add --json flags, convert print statements to structured JSON, build a CLI helper library, create an open-source CLI-for-agents package, add structured logging, or make CLI output machine-readable. Also use when the user mentions NDJSON, structured CLI output, agent-friendly CLI, non-interactive scripts, or JSON I/O for automation.

Why use Agent Cli on TypingMind?

Because you install it once and use it with any model. Agent Cli 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 Agent Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/claude-skills/tree/main/agent-cli. 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 Agent Cli?

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 Agent Cli?

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

Is the Agent Cli AI skill free?

Yes. It is published on GitHub by glebis 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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