dog-cli 2025.12.7.post2

Documentation Oriented Grammar (DOG) CLI Tool

DOG

Documentation Oriented Grammar — A Markdown-native format for system documentation that serves humans and AI agents alike.

Available on: pypi

---

Quick Start

Installation

pip install dog-cli
# or with uv
uv add dog-cli

Basic Usage

# Validate your docs
dog lint docs/

# Format files
dog format docs/

# Generate project index
dog index docs/ --name "My Project"

# Search for concepts
dog search "login" --path docs/

# Get a specific document
dog get "@User" --path docs/

# List all documents
dog list --path docs/

# Find what references a primitive
dog refs "#AuthService" --path docs/

# Generate dependency graph
dog graph --path docs/ | dot -Tpng -o graph.png

# Export all docs as JSON
dog export --path docs/ > context.json

# Serve documentation in browser
dog serve docs/

Agent System Prompt

Add this to your LLM agent's system prompt to enable DOG-driven development:

---
name: dog-developer
description: >
  PRIMARY DEVELOPMENT AGENT for this codebase. Use for ALL development tasks:
  implementing features, fixing bugs, refactoring, code reviews, and architectural decisions.

  This agent enforces DOG (Documentation-Oriented Grammar)—a documentation-first methodology
  where .dog.md behavioral specifications are the source of truth. Code fulfills documentation,
  not the other way around.
model: opus
---

You are the primary development agent for this codebase, combining expert software engineering with DOG methodology.

## DOG Overview

DOG fills the gap between unstructured docs and rigid schemas. Core insight: **describe behavior as constraints first, then design the system pattern around them before implementation.**

### Primitives

| Sigil | Type      | Purpose               | Required Sections                      |
| ----- | --------- | --------------------- | -------------------------------------- |
| `@`   | Actor     | Who initiates actions | Description, Notes                     |
| `!`   | Behavior  | What the system does  | Condition, Description, Outcome, Notes |
| `#`   | Component | How it's built        | Description, State, Events, Notes      |
| `&`   | Data      | What's stored         | Description, Fields, Notes             |

Example: "`@User` submits `&Order` to `#CartService`, triggering `!Checkout`"

## CLI Reference

**IMPORTANT**: Always use `-o json` for `search`, `get`, `list`, `refs` to get structured output.

| Command  | Usage                                 | Key Options                                                            |
| -------- | ------------------------------------- | ---------------------------------------------------------------------- |
| `search` | `uv run dog search <query> -o json`   | `-l` limit, `-p` path. Prefix with sigil to filter: `@query`, `!query` |
| `get`    | `uv run dog get <name> -o json`       | `-p` path. Use sigil: `@User`, `!Checkout`                             |
| `list`   | `uv run dog list [sigil] -o json`     | `-p` path. Filter: `@`, `!`, `#`, `&`                                  |
| `refs`   | `uv run dog refs <name> -o json`      | `-p` path. Reverse lookup: what references this?                       |
| `export` | `uv run dog export -p <path>`         | `-t` type filter, `--no-raw`. Bulk export for AI context.              |
| `graph`  | `uv run dog graph [root] -p <path>`   | DOT output. Pipe to graphviz: `\| dot -Tpng -o graph.png`              |
| `lint`   | `uv run dog lint <path>`              | Validate structure/refs. Run before commits.                           |
| `format` | `uv run dog format <path>`            | `--check` to verify without modifying                                  |
| `patch`  | `uv run dog patch <name> -d '<json>'` | Update sections: `'{"sections": {"Description": "..."}}'`              |
| `index`  | `uv run dog index <path> -n <name>`   | Regenerate index.dog.md                                                |

## Workflow

1. **Understand**: `dog get <name> -o json`, `dog list -o json` and `dog search -o json` to read relevant primitives
2. **Design**: Identify affected Behaviors; document new ones before coding
3. **Implement**: Code fulfills documented behavior
4. **Validate**: `dog lint docs` passes

**No docs?** Investigate code, document findings, then proceed.
**Bug fix?** Determine if bug is in code (doesn't match spec) or spec (wrong spec). Fix the right one.

## Quality Gate

Before completing any task:
- Code implements documented Behaviors
- `dog lint` passes
- All required sections present for new primitives
- Cross-references resolve to existing primitives

You advocate for documentation-first development. If asked to implement without clear specs, clarify or document the expected behavior first.

---

Example

See the docs/ folder for a complete example of DOG documentation for this project.

---

What is DOG?

.dog.md is a Markdown-native specification format. Each file defines exactly one primitive type — Project, Actor, Behavior, Component, or Data — using light structural conventions.

DOG serves as:

• Human-readable system documentation
• A structured knowledge base for LLM agents
• A behavioral reference for AI-assisted testing

Primitive Types

Type	Purpose	Example
Project	Root index of a documentation set	# Project: MyApp
Actor	User or service that initiates actions	# Actor: User
Behavior	System response or state transition	# Behavior: Login Flow
Component	Subsystem or UI element	# Component: AuthService
Data	Domain entity with fields	# Data: Credentials

Cross-References

Use sigils inside backticks to reference other concepts:

Syntax	Meaning
`@User`	Actor reference
`!Login`	Behavior reference
`#AuthService`	Component reference
`&Credentials`	Data reference

---

CLI Commands

dog lint <path>

Validate .dog.md files for structure and reference errors.

dog lint docs/
dog lint my-behavior.dog.md

dog format <path>

Format .dog.md files (normalize whitespace).

dog format docs/
dog format --check docs/  # Check without modifying

dog index <path> --name <name>

Generate or update a Project index file (index.dog.md).

dog index docs/ --name "My Project"

dog search <query>

Search documents using fuzzy matching. Returns top-k results sorted by relevance.

dog search "login"
dog search "#auth"              # Filter by Component type
dog search "user" --limit 5 --output json

Option	Description
--path, -p	Directory to search (default: .)
--limit, -l	Max results (default: 10)
--output, -o	text or json

Use sigil prefixes to filter by type: @ (Actor), ! (Behavior), # (Component), & (Data).

dog get <name>

Get a document by name with resolved references.

dog get "Login Flow"
dog get "@User"                 # Get Actor named User
dog get "#AuthService" --output json

Option	Description
--path, -p	Directory to search (default: .)
--output, -o	text or json

Use sigil prefixes to filter by type: @ (Actor), ! (Behavior), # (Component), & (Data).

dog list

List all documents.

dog list
dog list !                      # List only Behaviors
dog list --output json

Option	Description
--path, -p	Directory to search (default: .)
--output, -o	text or json

Use sigil prefixes to filter by type: @ (Actor), ! (Behavior), # (Component), & (Data).

dog patch <name>

Update specific sections of a DOG document programmatically.

dog patch "@User" --data '{"sections": {"Description": "Updated description"}}'
dog patch "Login" --data '{"sections": {"Outcome": "New outcome"}}'

Option	Description
--path, -p	Directory to search (default: .)
--data, -d	JSON patch data

dog refs <name>

Find all documents that reference a given primitive (reverse lookup).

dog refs "#AuthService"         # What references AuthService?
dog refs "@User" --output json  # JSON output

Option	Description
--path, -p	Directory to search (default: .)
--output, -o	text or json

Use sigil prefixes to filter by type: @ (Actor), ! (Behavior), # (Component), & (Data).

dog graph [root]

Generate a DOT format dependency graph for visualization.

dog graph                              # Full graph
dog graph "!Login"                     # Subgraph from Login behavior
dog graph -p docs/ | dot -Tpng -o graph.png  # Render with graphviz

Option	Description
--path, -p	Directory to search (default: .)

Output is DOT format, pipe to graphviz (dot, neato, etc.) for rendering.

dog export

Export all DOG documents as JSON for AI agent consumption.

dog export -p docs/                    # Export all docs
dog export -t ! -p docs/               # Export only Behaviors
dog export --no-raw -p docs/           # Exclude raw markdown

Option	Description
--path, -p	Directory to search (default: .)
--type, -t	Type filter: @ (Actor), ! (Behavior), #, &
--no-raw	Exclude raw markdown content from output

dog serve <path>

Serve DOG documentation as HTML in the browser with hot-reload.

dog serve docs/
dog serve --host 0.0.0.0 --port 3000
dog serve docs/ --no-reload

Option	Description
--host, -h	Host to bind (default: 127.0.0.1)
--port, -P	Port to bind (default: 8000)
--no-reload	Disable hot-reload on file changes

Features:

• Color-coded reference links (red=Actor, blue=Behavior, purple=Component, green=Data)
• Renders index.dog.md as homepage when present
• Automatic favicon discovery (favicon.png or dog.png)
• Hot-reload on file changes

---

File Format Reference

Project

# Project: <Name>

## Description
<freeform text>

## Actors
- <actor name>

## Behaviors
- <behavior name>

## Components
- <component name>

## Data
- <data name>

## Notes
- <annotation>

Actor

# Actor: <Name>

## Description
<free text>

## Notes
- <annotation>

Behavior

# Behavior: <Name>

## Condition
- <prerequisite>

## Description
<free text with `@actor`, `!behavior`, `#component`, `&data` references>

## Outcome
- <expected effect>

## Notes
- <annotation>

Component

# Component: <Name>

## Description
<free text>

## State
- <state field>

## Events
- <event name>

## Notes
- <annotation>

Data

# Data: <Name>

## Description
<free text>

## Fields
- field_name: <description>

## Notes
- <annotation>

---

Why DOG?

Approach	Trade-offs
RAG/Vector Search	Requires embeddings, chunking strategy, retrieval tuning. Context can be fragmented or miss cross-references.
Traditional Docs	Great for humans, but unstructured prose is hard for LLMs to reliably extract structured knowledge from.
OpenAPI/JSON Schema	Excellent for API contracts, but doesn't capture behavioral flows, actors, or domain concepts.
DOG	Markdown-native, no infra needed. Structured enough for LLM parsing, readable enough for humans. Single source of truth.

DOG fills the gap between unstructured documentation and rigid schemas. It's lightweight enough to write by hand, structured enough to parse programmatically, and readable enough to serve as your actual documentation.

---

License

MIT