> ## Documentation Index
> Fetch the complete documentation index at: https://agentharnesses.io/llms.txt
> Use this file to discover all available pages before exploring further.

# CLI

> Install and use the agentharnesses CLI to scaffold and manage harnesses.

# CLI

The `ahar` CLI scaffolds and manages Agent Harnesses.

## Installation

```bash theme={null}
pip install agentharnesses-cli
```

## Commands

### `ahar init`

Scaffold a new harness in the current directory:

```bash theme={null}
ahar init
```

Optionally specify a name (defaults to the directory name):

```bash theme={null}
ahar init my-harness
```

This creates:

```
my-harness/
├── HARNESS.md                       # entry point and agent identity
├── README.md                        # human-facing description
├── .gitignore
├── .claude/settings.json            # registers the harness as a Claude Code plugin
├── skills/
│   └── SKILLS.md                    # skill index
└── references/
    └── REFERENCES.md                # reference index
```

When using the `claude` preset (default), `ahar init` also installs:

```
├── .claude/skills/agent-harnesses/  # metaskill for progressive harness exploration
└── skills/
    └── maintenance/
        ├── SKILLS.md
        └── modify-harness/
            └── SKILL.md
```

The metaskill is cloned fresh from [agentharnesses/metaskill](https://github.com/agentharnesses/metaskill) at init time.

### `ahar validate`

Validate a harness directory structure:

```bash theme={null}
ahar validate ./my-harness
```

### `ahar read`

Read a property from a harness's `HARNESS.md` frontmatter:

```bash theme={null}
ahar read ./my-harness name
ahar read ./my-harness description
```

### `ahar prompt`

Render a harness as prompt XML for agent injection:

```bash theme={null}
ahar prompt ./my-harness
```

These commands are backed by [harnesses-ref](https://pypi.org/project/harnesses-ref/), the reference implementation for the Agent Harnesses standard.