coding-assistant-cli 0.1.8

An agent-orchestrated CLI for streamlining and automating coding tasks.

Coding Assistant

Coding Assistant is a Python-based, agent-orchestrated CLI that helps you automate and streamline coding tasks. It can plan, launch sub-agents, use MCP tools (filesystem, web fetch/search, Context7, Tavily, etc.), run inside a sandbox, and keep resumable history.

Key Features

• Orchestrator agent that delegates to sub-agents and tools
• Resumable sessions and conversation summaries stored per-project
• Built-in MCP server with shell, Python, filesystem, and TODO tools
• Support for external MCP servers (filesystem, fetch, Context7, Tavily, etc.)
• Landlock-based filesystem sandbox with readable/writable allowlists
• Prompt-toolkit powered TUI with dense and regular output modes
• Shell/tool confirmation patterns to guard dangerous operations
• Chat mode enabled by default for interactive conversations
• Configurable via CLI flags (models, planning mode, instructions, etc.)

Requirements

• Python 3.12+

• uv (recommended) or pip for running/installing

• Optional: External MCP servers if you want to extend functionality

  • Node.js/npm for npx (for NPM-based MCP servers)
  • Network access to fetch packages

• API keys as needed by your chosen model/tooling, e.g.:

  • OPENAI_API_KEY (or other LiteLLM-compatible provider keys)
  • Additional keys for external MCP servers (e.g., TAVILY_API_KEY)

Installation

Using uv (recommended):

uv tool install coding-assistant-cli

Quickstart

The easiest way to run is with the uv command:

coding-assistant \
  --model "openrouter/anthropic/claude-3.5-sonnet" \
  --task "Refactor all function names to snake_case."

Resume the last session:

coding-assistant \
  --model "openrouter/anthropic/claude-3.5-sonnet" \
  --resume

Show available options:

coding-assistant --help

Advanced Examples

You can invoke the CLI with additional MCP servers (stdio or SSE/remote). The built-in coding_assistant.mcp is included by default.

coding-assistant \
  --model "openrouter/openai/gpt-4o-mini" \
  --task "Say 'Hello World'" \
  --mcp-servers \
    '{"name": "filesystem", "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "{home_directory}"]}' \
    '{"name": "fetch", "command": "uvx", "args": ["mcp-server-fetch"]}'

Notes:

• Model names are routed via LiteLLM. Use any provider/model you have keys for; set the corresponding API key env vars.
• The --mcp-servers values are JSON strings; arguments support variable substitution for {home_directory} and {working_directory}.

Usage Highlights

• --model Select model for the orchestrator agent (required).
• --expert-model Select model for expert tasks (defaults to the same value as --model).
• --tool-confirmation-patterns / --shell-confirmation-patterns Regex patterns to require user confirmation before tool/shell execution.
• --chat-mode / --no-chat-mode Enable/disable open-ended chat mode (default: enabled).
• --resume / --resume-file Resume from the latest/specific orchestrator history in .coding_assistant/history/.
• --instructions Provide extra instructions that are composed with defaults. Can be repeated.
• --compact-conversation-at-tokens Number of tokens after which conversation should be shortened (default: 200,000).
• --trace / --no-trace Enable/disable tracing of model requests/responses.
• --sandbox / --no-sandbox Enable/disable Landlock-based sandboxing (default: enabled).
• --wait-for-debugger Wait for a debugger (debugpy) to attach on port 1234.

Run coding-assistant --help to see all options.

MCP Servers

Pass MCP servers with repeated --mcp-servers flags as JSON strings. Support is provided for both stdio-based servers and remote servers via SSE (Server-Sent Events).

Stdio Server

{
  "name": "filesystem",
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "{home_directory}"]
}

Remote Server (SSE)

{
  "name": "remote-mcp",
  "url": "http://localhost:8000/sse"
}

Built-in: Coding Assistant MCP

This repository includes a built-in MCP server (package coding_assistant.mcp) that is started automatically by default. It provides:

• shell: shell_execute — Execute shell commands with timeout and output truncation
• python: python_execute — Execute Python code with timeout and output truncation
• filesystem: filesystem_write_file, filesystem_edit_file — Write new files or apply targeted edits
• todo: todo_add, todo_list_todos, todo_complete — Simple in-memory TODO list management

When connected, tools are exposed to the agent as fully-qualified names (e.g., shell_execute).

External MCP Servers (Optional)

You can add external MCP servers to extend functionality. Examples include:

• filesystem: @modelcontextprotocol/server-filesystem (NPM) — Additional filesystem operations
• fetch: mcp-server-fetch (uvx) — Web fetching capabilities
• context7: @upstash/context7-mcp (NPM) — Context management
• tavily: tavily-mcp (needs TAVILY_API_KEY) — Web search

To use these, add them via --mcp-servers flags as shown in the examples above.

You can print all discovered tools from running MCP servers:

coding-assistant --print-mcp-tools --mcp-servers '...'

Sandbox

When enabled (default), the assistant applies Landlock restrictions. By default it adds:

• Readable directories: your active virtual environment and any passed via --readable-sandbox-directories.
• Writable directories: the current working directory and any passed via --writable-sandbox-directories.

Use these flags to widen access if needed when working across multiple directories or mounts.

Example:

--readable-sandbox-directories /mnt/wsl ~/.ssh ~/.rustup \
--writable-sandbox-directories "$project_dir" /tmp /dev/shm ~/.cache/coding_assistant

History and Resume

• Conversation history and summaries are kept under .coding_assistant/ in your project.
• Use --resume to continue from the most recent session, or --resume-file to select a specific file.
• The assistant automatically trims old history once it's saved.

Shell command execution behavior

The built-in MCP tools shell_execute and python_execute:

• Support multi-line scripts
• Merge stderr into stdout and return plain text (no JSON envelope)
• Prefix output with Exit code: N only when the command/code exits non-zero
• Support truncate_at parameter to limit combined output size and append a note when truncation occurs
• Support timeout parameter (default: 30 seconds)
• Interactive commands (e.g., git rebase -i) are not supported and will block

Development

• Run tests:

  just test
  

  Run integration tests:

  just test-integration
  

  or manually:

  uv run pytest -n auto -m "not slow"
  

• Run linting/formatting/type-checking:

  just lint
  

• Handy just recipes: test, lint (see justfile).

Skills

Coding Assistant includes a skills system that provides specialized workflows, tool integrations, and domain expertise for common tasks. Skills transform the general-purpose agent into a specialized one by bundling instructions, scripts, references, and assets.

Available Skills

The following skills are available:

• planning - Guidelines for iteratively planning tasks and changes before implementation. Use when the user requests a non-trivial task or when you need to align on a complex implementation strategy.

• code-review - Provides a structured workflow for planning and executing code reviews like a senior engineer, including checklists for quality, security, and maintainability. Use when asked to review code, PRs, or plan a code review task.

• create-skill - Guide for creating new Agent Skills. Use this skill when you need to create a new skill to extend the agent's capabilities with specialized knowledge, workflows, or tool integrations.

• developing - General principles for exploring, developing, editing, and refactoring code. Use for codebase analysis, implementation tasks, and code quality improvements.

How Skills Work

Skills use a progressive disclosure system:

1. Metadata (name + description) - Always loaded, ~100 words
2. SKILL.md body - Loaded when skill triggers, <500 lines
3. Bundled resources - Loaded as needed (scripts, references, assets)

When a skill's context matches the current task, it loads automatically, providing specialized instructions and tools.

Skill Structure

Skills are organized as directories containing:

skill-name/
├── SKILL.md          # Required: instructions + metadata
├── scripts/          # Optional: executable code
├── references/       # Optional: documentation
└── assets/           # Optional: templates/resources

License

MIT