clawd-code-python-port

Imported from reason-machines/trending-skills (skills/clawd-code-python-port/SKILL.md) via skills.sh. Install upstream with npx skills add reason-machines/trending-skills --skill clawd-code-python-port. Copyright stays with the author.

clawd-code Python Port

Skill by ara.so — Daily 2026 Skills collection.

What This Project Does

clawd-code is an independent Python rewrite of the Claude Code agent harness, built from scratch for educational purposes. It captures the architectural patterns of Claude Code — tool wiring, command dispatch, task orchestration, and agent runtime context — in clean Python, without copying any proprietary TypeScript source.

The project is orchestrated end-to-end using oh-my-codex (OmX), a workflow layer on top of OpenAI Codex. It is not affiliated with or endorsed by Anthropic.

Installation

# Clone the repository
git clone https://github.com/instructkr/clawd-code.git
cd clawd-code

# (Optional but recommended) Create a virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies (if a requirements.txt or pyproject.toml is present)
pip install -r requirements.txt
# or
pip install -e .

No API keys are needed for the manifest/summary/CLI commands. If you extend the query engine to call a live model, set your key via environment variable:

export ANTHROPIC_API_KEY="your-key-here"
export OPENAI_API_KEY="your-key-here"

Repository Layout

.
├── src/
│   ├── __init__.py
│   ├── commands.py       # Command port metadata
│   ├── main.py           # CLI entrypoint
│   ├── models.py         # Dataclasses: subsystems, modules, backlog
│   ├── port_manifest.py  # Python workspace structure summary
│   ├── query_engine.py   # Renders porting summary from active workspace
│   ├── task.py           # Task orchestration primitives
│   └── tools.py          # Tool port metadata
├── tests/                # unittest-based verification
└── assets/

Key CLI Commands

All commands run via python3 -m src.main <subcommand>.

# Print a human-readable porting summary
python3 -m src.main summary

# Print the current Python workspace manifest
python3 -m src.main manifest

# List current Python modules/subsystems (paginated)
python3 -m src.main subsystems --limit 16

# Inspect mirrored command inventory
python3 -m src.main commands --limit 10

# Inspect mirrored tool inventory
python3 -m src.main tools --limit 10

# Run parity audit against local ignored archive (when present)
python3 -m src.main parity-audit

# Run the full test suite
python3 -m unittest discover -s tests -v

Core Data Models (src/models.py)

The dataclasses define the shape of the porting workspace:

from dataclasses import dataclass, field
from typing import List, Optional

@dataclass
class Module:
    name: str
    status: str          # e.g. "ported", "stub", "backlog"
    source_path: str
    notes: Optional[str] = None

@dataclass
class Subsystem:
    name: str
    modules: List[Module] = field(default_factory=list)
    description: Optional[str] = None

@dataclass
class PortManifest:
    subsystems: List[Subsystem] = field(default_factory=list)
    backlog: List[str] = field(default_factory=list)
    version: str = "0.1.0"

Tools System (src/tools.py)

Tools are the callable units in the agent harness. Each tool entry carries metadata for dispatch:

from dataclasses import dataclass
from typing import Callable, Optional, Any, Dict

@dataclass
class Tool:
    name: str
    description: str
    parameters: Dict[str, Any]          # JSON-schema style param spec
    handler: Optional[Callable] = None  # Python callable for this tool

# Example: registering a tool
def read_file_handler(path: str) -> str:
    with open(path, "r") as f:
        return f.read()

READ_FILE_TOOL = Tool(
    name="read_file",
    description="Read the contents of a file at the given path.",
    parameters={
        "path": {"type": "string", "description": "Absolute or relative file path"}
    },
    handler=read_file_handler,
)

# Tool registry pattern
TOOL_REGISTRY: Dict[str, Tool] = {
    READ_FILE_TOOL.name: READ_FILE_TOOL,
}

def dispatch_tool(name: str, **kwargs) -> Any:
    tool = TOOL_REGISTRY.get(name)
    if tool is None:
        raise ValueError(f"Unknown tool: {name}")
    if tool.handler is None:
        raise NotImplementedError(f"Tool '{name}' has no handler yet.")
    return tool.handler(**kwargs)

Commands System (src/commands.py)

Commands are higher-level agent actions, distinct from raw tools:

from dataclasses import dataclass
from typing import Optional, Callable, Any

@dataclass
class Command:
    name: str
    description: str
    aliases: list
    handler: Optional[Callable] = None

# Example command
def summarize_handler(context: dict) -> str:
    return f"Summarizing {len(context.get('files', []))} files."

SUMMARIZE_COMMAND = Command(
    name="summarize",
    description="Summarize the current workspace context.",
    aliases=["sum", "overview"],
    handler=summarize_handler,
)

COMMAND_REGISTRY = {
    SUMMARIZE_COMMAND.name: SUMMARIZE_COMMAND,
}

def run_command(name: str, context: dict) -> Any:
    cmd = COMMAND_REGISTRY.get(name)
    if not cmd:
        raise ValueError(f"Unknown command: {name}")
    if not cmd.handler:
        raise NotImplementedError(f"Command '{name}' not yet implemented.")
    return cmd.handler(context)

Task Orchestration (src/task.py)

Tasks wrap a unit of agent work — a goal, a set of tools, and a result:

from dataclasses import dataclass, field
from typing import List, Optional, Any

@dataclass
class TaskResult:
    success: bool
    output: Any
    error: Optional[str] = None

@dataclass
class Task:
    goal: str
    tools: List[str] = field(default_factory=list)   # tool names available
    context: dict = field(default_factory=dict)
    result: Optional[TaskResult] = None

    def run(self, dispatcher) -> TaskResult:
        """
        dispatcher: callable(tool_name, **kwargs) -> Any
        Implement your agent loop here.
        """
        try:
            # Minimal stub: just report goal received
            output = f"Task received: {self.goal}"
            self.result = TaskResult(success=True, output=output)
        except Exception as e:
            self.result = TaskResult(success=False, output=None, error=str(e))
        return self.result

# Usage
from src.tools import dispatch_tool

task = Task(
    goal="Read README.md and summarize it",
    tools=["read_file"],
    context={"working_dir": "."},
)
result = task.run(dispatcher=dispatch_tool)
print(result.output)

Query Engine (src/query_engine.py)

The query engine renders a porting summary from the active manifest:

from src.port_manifest import build_manifest
from src.query_engine import render_summary

manifest = build_manifest()
summary = render_summary(manifest)
print(summary)

You can also invoke it from the CLI:

python3 -m src.main summary

Port Manifest (src/port_manifest.py)

Build and inspect the current workspace manifest programmatically:

from src.port_manifest import build_manifest

manifest = build_manifest()

for subsystem in manifest.subsystems:
    print(f"[{subsystem.name}]")
    for module in subsystem.modules:
        print(f"  {module.name}: {module.status}")

print("Backlog:", manifest.backlog)

Adding a New Tool

1. Define a handler function in src/tools.py.
2. Create a Tool dataclass instance.
3. Register it in TOOL_REGISTRY.
4. Write a test in tests/.

# src/tools.py

def list_dir_handler(path: str):
    import os
    return os.listdir(path)

LIST_DIR_TOOL = Tool(
    name="list_dir",
    description="List files in a directory.",
    parameters={"path": {"type": "string"}},
    handler=list_dir_handler,
)

TOOL_REGISTRY["list_dir"] = LIST_DIR_TOOL

Adding a New Command

# src/commands.py

def lint_handler(context: dict) -> str:
    files = context.get("files", [])
    return f"Linting {len(files)} files (stub)."

LINT_COMMAND = Command(
    name="lint",
    description="Lint the current workspace files.",
    aliases=["check"],
    handler=lint_handler,
)

COMMAND_REGISTRY["lint"] = LINT_COMMAND

Running Tests

# Run all tests with verbose output
python3 -m unittest discover -s tests -v

# Run a specific test file
python3 -m unittest tests.test_tools -v

Example test pattern:

# tests/test_tools.py
import unittest
from src.tools import dispatch_tool
import tempfile, os

class TestReadFileTool(unittest.TestCase):
    def test_read_file(self):
        with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
            f.write("hello clawd")
            path = f.name
        try:
            result = dispatch_tool("read_file", path=path)
            self.assertEqual(result, "hello clawd")
        finally:
            os.unlink(path)

if __name__ == "__main__":
    unittest.main()

Parity Audit

When a local ignored archive of the original snapshot is present, run:

python3 -m src.main parity-audit

This compares the current Python workspace surface against the archived root-entry file surface, subsystem names, and command/tool inventories, reporting gaps.

Common Patterns

Chaining tools in a task loop

from src.tools import dispatch_tool
from src.task import Task

task = Task(
    goal="Read and list files",
    tools=["read_file", "list_dir"],
    context={"working_dir": "."},
)

# Manual tool chain (before full agent loop is implemented)
files = dispatch_tool("list_dir", path=".")
for fname in files[:3]:
    content = dispatch_tool("read_file", path=fname)
    print(f"--- {fname} ---\n{content[:200]}")

Using the manifest in automation

from src.port_manifest import build_manifest

def unported_modules():
    manifest = build_manifest()
    stubs = []
    for sub in manifest.subsystems:
        for mod in sub.modules:
            if mod.status != "ported":
                stubs.append((sub.name, mod.name, mod.status))
    return stubs

for subsystem, module, status in unported_modules():
    print(f"{subsystem}/{module} → {status}")

Troubleshooting

Key Links

• Repository: https://github.com/instructkr/clawd-code
• oh-my-codex (OmX): https://github.com/Yeachan-Heo/oh-my-codex
• Related essay: Is legal the same as legitimate?
• Not affiliated with Anthropic.