feat(analysis): Implement project analysis engine and CI/CD workflow

This commit introduces a new project analysis engine to the DSS.

Key features include:
- A new analysis module in `dss-mvp1/dss/analyze` that can parse React projects and generate a dependency graph.
- A command-line interface (`dss-mvp1/dss-cli.py`) to run the analysis, designed for use in CI/CD pipelines.
- A new `dss_project_export_context` tool in the Claude MCP server to allow AI agents to access the analysis results.
- A `.gitlab-ci.yml` file to automate the analysis on every push, ensuring the project context is always up-to-date.
- Tests for the new analysis functionality.

This new architecture enables DSS to have a deep, version-controlled understanding of a project's structure, which can be used to power more intelligent agents and provide better developer guidance. The analysis is no longer automatically triggered on `init`, but is designed to be run manually or by a CI/CD pipeline.

dss-mvp1/tests/conftest.py

@@ -0,0 +1,82 @@
+import pytest
+from pathlib import Path
+
+@pytest.fixture(scope="function")
+def mock_react_project(tmp_path: Path) -> Path:
+    """
+    Creates a temporary mock React project structure for testing.
+    """
+    project_dir = tmp_path / "test-project"
+    project_dir.mkdir()
+
+    # Create src directory
+    src_dir = project_dir / "src"
+    src_dir.mkdir()
+
+    # Create components directory
+    components_dir = src_dir / "components"
+    components_dir.mkdir()
+
+    # Component A
+    (components_dir / "ComponentA.jsx").write_text("""
+import React from 'react';
+import './ComponentA.css';
+
+const ComponentA = () => {
+    return <div className="component-a">Component A</div>;
+};
+
+export default ComponentA;
+    """)
+
+    (components_dir / "ComponentA.css").write_text("""
+.component-a {
+    color: blue;
+}
+    """)
+
+    # Component B
+    (components_dir / "ComponentB.tsx").write_text("""
+import React from 'react';
+import ComponentA from './ComponentA';
+
+const ComponentB = () => {
+    return (
+        <div>
+            <ComponentA />
+        </div>
+    );
+};
+
+export default ComponentB;
+    """)
+
+    # App.js
+    (src_dir / "App.js").write_text("""
+import React from 'react';
+import ComponentB from './components/ComponentB';
+
+function App() {
+    return (
+        <div className="App">
+            <ComponentB />
+        </div>
+    );
+}
+
+export default App;
+    """)
+    
+    # package.json
+    (project_dir / "package.json").write_text("""
+{
+    "name": "test-project",
+    "version": "0.1.0",
+    "private": true,
+    "dependencies": {
+        "react": "^18.0.0"
+    }
+}
+    """)
+
+    return project_dir

dss-mvp1/tests/test_project_analyzer.py

@@ -0,0 +1,45 @@
+import pytest
+import json
+from pathlib import Path
+from dss.analyze.project_analyzer import run_project_analysis
+
+def test_run_project_analysis(mock_react_project: Path):
+    """
+    Tests the run_project_analysis function to ensure it creates the analysis graph
+    and that the graph contains the expected file nodes.
+    """
+    # Run the analysis on the mock project
+    run_project_analysis(str(mock_react_project))
+
+    # Check if the analysis file was created
+    analysis_file = mock_react_project / ".dss" / "analysis_graph.json"
+    assert analysis_file.exists(), "The analysis_graph.json file was not created."
+
+    # Load the analysis data
+    with open(analysis_file, 'r') as f:
+        data = json.load(f)
+
+    # Verify the graph structure
+    assert "nodes" in data, "Graph data should contain 'nodes'."
+    assert "links" in data, "Graph data should contain 'links'."
+
+    # Get a list of node IDs (which are the relative file paths)
+    node_ids = [node['id'] for node in data['nodes']]
+
+    # Check for the presence of the files from the mock project
+    expected_files = [
+        "package.json",
+        "src/App.js",
+        "src/components/ComponentA.css",
+        "src/components/ComponentA.jsx",
+        "src/components/ComponentB.tsx",
+    ]
+
+    for file_path in expected_files:
+        # Path separators might be different on different OSes, so we normalize
+        normalized_path = str(Path(file_path))
+        assert normalized_path in node_ids, f"Expected file '{normalized_path}' not found in the analysis graph."
+
+    # Verify the number of nodes
+    # There should be exactly the number of files we created
+    assert len(node_ids) == len(expected_files), "The number of nodes in the graph does not match the number of files."