jaymd96-eyeball 0.1.0

Code introspection CLI for LLM-assisted development with structured JSON output

eyeball

Code introspection CLI for LLM-assisted development with structured JSON output.

eyeball gives LLMs "eyes" to see into your code - exploring modules, verifying API usage, running quick probes, and analyzing dependencies.

Installation

# Install as a dev dependency
pip install jaymd96-eyeball

# Or with optional dependencies for full functionality
pip install "jaymd96-eyeball[dev,toml]"

Quick Start

# Discover all public items in a module
eyeball discover mypackage.models

# Inspect a class or function
eyeball inspect mypackage.models:User

# Run a function with arguments
eyeball run mypackage.utils:process_data --kwargs '{"input": "test"}'

# Execute arbitrary code
eyeball exec 'from mypackage import VERSION; print(VERSION)'

# Run tests with structured output
eyeball test mypackage.models

# Quick verification probe
eyeball probe "assert 1 + 1 == 2"

# Analyze dependencies of a function/class
eyeball deps mypackage.models:User

# Find all callers of a target
eyeball callers mypackage.models:User

# Get API documentation for third-party libraries
eyeball api requests:get

Commands

Command	Description
discover	List all public classes and functions in a module
inspect	Get detailed information about a module, class, or function
run	Execute a function or instantiate a class
exec	Execute arbitrary Python code
test	Run pytest with structured JSON output
source	Show source code of a target
reload	Hot-reload a module after changes
list-tests	List all test files in the project
probe	Run quick verification probes (lightweight testing)
fixtures	List available fixtures for probes
deps	Analyze dependencies of a function/class using AST
callers	Find all references to a target (reverse dependencies)
module-deps	Analyze all imports in a module
api	Get comprehensive API docs (works on third-party libs)
search-api	Search for items in a module by name
help-json	Output help as JSON for LLM consumption

Configuration

Configure eyeball via pyproject.toml:

[tool.eyeball]
package_name = "mypackage"           # Auto-detected from project.name
tests_dir = "tests"                  # Test directory (default: "tests")
fixtures_module = "mypackage.fixtures"  # Custom fixtures module

Or use a standalone eyeball.toml file with the same options.

Custom Fixtures

Create custom fixtures for your probes:

# mypackage/fixtures.py
from eyeball.harness import Fixtures

@Fixtures.register("database")
def database_fixture():
    """In-memory database for testing."""
    from mypackage.db import create_test_db
    db = create_test_db()
    return {
        "db": db,
        "create_user": db.create_user,
        "get_user": db.get_user,
    }

@Fixtures.register("sample_data")
def sample_data_fixture():
    """Sample test data."""
    return {
        "users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}],
        "products": [{"id": 1, "name": "Widget", "price": 9.99}],
    }

Then configure:

[tool.eyeball]
fixtures_module = "mypackage.fixtures"

Use in probes:

eyeball probe --fixture database "assert db.get_user(1) is not None"
eyeball probe --fixture sample_data "assert len(users) == 2"

JSON Output

All commands output JSON for easy parsing by LLMs and scripts:

# Pretty-print with -p flag
eyeball -p inspect mypackage.models:User

{
  "status": "success",
  "target": "mypackage.models:User",
  "type": "class",
  "class_name": "User",
  "attributes": [
    {"name": "id", "type": "int", "required": true},
    {"name": "name", "type": "str", "required": true},
    {"name": "email", "type": "str", "required": false}
  ],
  "methods": [...]
}

API Introspection

Explore third-party libraries to verify correct usage:

# Search for functions in a library
eyeball -p search-api requests get

{
  "status": "success",
  "module": "requests",
  "query": "get",
  "results": [
    {"name": "get", "type": "function", "signature": "(url, params=None, **kwargs)", "summary": "Sends a GET request."}
  ]
}

# Get full API documentation
eyeball -p api requests:get

{
  "status": "success",
  "target": "requests:get",
  "type": "function",
  "name": "get",
  "signature": "(url, params=None, **kwargs)",
  "parameters": [
    {"name": "url", "required": true, "kind": "POSITIONAL_OR_KEYWORD", "description": "URL for the new Request object."},
    {"name": "params", "required": false, "default": "None", "description": "Dictionary or bytes to send in the query string."},
    {"name": "kwargs", "required": false, "kind": "VAR_KEYWORD", "description": "Optional arguments that request takes."}
  ],
  "return_type": "Response",
  "summary": "Sends a GET request.",
  "source_available": true,
  "source_preview": "def get(url, params=None, **kwargs):\n    ..."
}

Works on any importable Python code - your project, stdlib, or third-party packages.

Dependency Analysis

Analyze what a function or class depends on:

eyeball -p deps mypackage.models:User

{
  "status": "success",
  "target": "mypackage.models:User",
  "target_type": "class",
  "imports": [
    {"module": "dataclasses", "name": "dataclass"},
    {"module": "typing", "name": "Optional"}
  ],
  "calls": [
    {"name": "field", "is_method": false, "args_count": 0, "kwargs_count": 1}
  ],
  "instantiations": [
    {"name": "UUID", "is_method": false}
  ],
  "summary": {
    "imports": 2,
    "calls": 3,
    "instantiations": 1
  }
}

Find all code that uses a target:

eyeball -p callers mypackage.models:User

{
  "status": "success",
  "target": "mypackage.models:User",
  "callers": [
    {
      "file": "mypackage/services/user_service.py",
      "references": [
        {"type": "import_from", "line": 3, "text": "from mypackage.models import User"},
        {"type": "name", "line": 15, "context": "load"}
      ]
    }
  ],
  "total_files": 5,
  "total_references": 12
}

Analyze module-level imports:

eyeball -p module-deps mypackage.models

Use with LLMs

eyeball is designed for LLM-assisted development workflows:

1. Exploration: LLMs can use discover and inspect to understand your codebase
2. Verification: Quick probe commands let LLMs verify their understanding
3. Testing: Structured test output helps LLMs understand test failures
4. Hot-reloading: reload enables rapid iteration without restarts
5. Dependency Analysis: deps and callers help understand code relationships
6. API Introspection: api and search-api let LLMs verify correct library usage

Built-in Fixtures

Fixture	Description	Provides
mocks	Mocking utilities	Mock, MagicMock, patch
async_helpers	Async testing	asyncio, run_async
temp_files	Temporary files	temp_dir, temp_file, Path

License

MIT