# Future Enhancement Ideas for Crackerjack

## 🔧 Advanced Auto-Init Detection

### 1. Tool Version Checks

Automatically detect when development tools have newer versions available.

```python
def _check_tool_updates(self) -> bool:
    """Check if any tools have updates available."""
    tools_to_check = {
        "ruff": self._get_ruff_version,
        "pyright": self._get_pyright_version,
        "pre-commit": self._get_precommit_version,
        "uv": self._get_uv_version,
    }

    for tool_name, version_getter in tools_to_check.items():
        current = version_getter()
        latest = self._fetch_latest_version(tool_name)

        if current < latest:
            self.console.print(
                f"[yellow]🔄 {tool_name} update available: "
                f"{current} → {latest}[/yellow]"
            )
            return True

    return False


def _fetch_latest_version(self, tool: str) -> str:
    """Fetch latest version from PyPI or GitHub."""
    # Could use httpx to check PyPI API
    # Or parse GitHub releases
    pass
```

**Benefits**:

- Always use latest tool improvements
- Security patches applied promptly
- New features available immediately

### 2. Configuration Drift Detection

Detect when configuration files have been manually edited and may not meet standards.

```python
def _check_config_integrity(self) -> bool:
    """Verify configuration files match expected standards."""
    config_checksums = {
        ".pre-commit-config.yaml": self._calculate_config_checksum(),
        "pyproject.toml": self._calculate_pyproject_checksum(),
    }

    expected_checksums = self._load_expected_checksums()

    for file, checksum in config_checksums.items():
        if checksum != expected_checksums.get(file):
            self.console.print(f"[yellow]⚠️ {file} has been modified manually[/yellow]")
            return True

    # Also check for required sections
    if not self._has_required_config_sections():
        return True

    return False


def _has_required_config_sections(self) -> bool:
    """Check if all required configuration sections exist."""
    pyproject = self.pkg_path / "pyproject.toml"
    if pyproject.exists():
        config = loads(pyproject.read_text())
        required = ["tool.ruff", "tool.pyright", "tool.pytest.ini_options"]
        return all(section in config for section in required)
    return False
```

**Benefits**:

- Prevents configuration decay
- Maintains consistency across team
- Catches accidental deletions

### 3. Smart Scheduling

Implement intelligent scheduling based on project activity and team preferences.

```python
def _should_scheduled_init(self) -> bool:
    """Check if scheduled initialization is due."""
    # Check environment for team preferences
    init_schedule = os.environ.get("CRACKERJACK_INIT_SCHEDULE", "weekly")
    init_day = os.environ.get("CRACKERJACK_INIT_DAY", "monday")

    if init_schedule == "weekly":
        today = datetime.now().strftime("%A").lower()
        if today == init_day.lower():
            last_init = self._get_last_init_timestamp()
            if datetime.now() - last_init > timedelta(days=6):
                return True

    elif init_schedule == "commit-based":
        # Run after every N commits
        commits_since_init = self._count_commits_since_init()
        threshold = int(os.environ.get("CRACKERJACK_INIT_COMMITS", "50"))
        if commits_since_init >= threshold:
            return True

    elif init_schedule == "activity-based":
        # Run when project is active (commits in last 24h)
        if self._has_recent_activity() and self._days_since_init() >= 7:
            return True

    return False


def _has_recent_activity(self) -> bool:
    """Check if project has recent git activity."""
    result = subprocess.run(
        ["git", "log", "-1", "--since=24.hours"], capture_output=True, text=True
    )
    return bool(result.stdout.strip())
```

**Benefits**:

- Team-specific scheduling
- Activity-aware updates
- Flexible timing options

### 4. Git Hook Integration

Add git hooks to check initialization status at key moments.

```bash
#!/bin/bash
# .git/hooks/pre-commit

# Check if init is needed before allowing commit
python -m crackerjack._internal.check_init_needed
if [ $? -eq 1 ]; then
    echo "⚠️  Project initialization needed!"
    echo "Run: python -m crackerjack /crackerjack-init"
    echo "Or commit with --no-verify to skip"
    exit 1
fi
```

```python
# crackerjack/_internal/check_init_needed.py
def main():
    """Check if initialization is needed."""
    from pathlib import Path
    import sys

    # Quick checks without full crackerjack import
    project_root = Path.cwd()

    # Check critical files
    if not (project_root / "pyproject.toml").exists():
        sys.exit(1)

    # Check age of pre-commit config
    pre_commit = project_root / ".pre-commit-config.yaml"
    if pre_commit.exists():
        age_days = (time.time() - pre_commit.stat().st_mtime) / 86400
        if age_days > 30:
            sys.exit(1)

    sys.exit(0)
```

**Benefits**:

- Catches issues before commits
- Gentle reminder system
- Can be bypassed when needed

### 5. Dependency Update Detection

Monitor for security updates and major dependency changes.

```python
def _check_dependency_updates(self) -> bool:
    """Check for important dependency updates."""
    # Parse pyproject.toml dependencies
    dependencies = self._parse_dependencies()

    # Check for security advisories
    vulnerabilities = self._check_safety_db(dependencies)
    if vulnerabilities:
        self.console.print(
            f"[red]🚨 Security updates available for: "
            f"{', '.join(vulnerabilities)}[/red]"
        )
        return True

    # Check for major version updates
    major_updates = self._check_major_updates(dependencies)
    if major_updates and self._should_notify_major_updates():
        return True

    return False


def _check_safety_db(self, deps: dict[str, str]) -> list[str]:
    """Check dependencies against security database."""
    # Could integrate with safety-db or GitHub advisory database
    # Return list of packages with vulnerabilities
    pass
```

**Benefits**:

- Security-first approach
- Controlled major version updates
- Dependency health monitoring

### 6. Project Health Metrics

Track project health indicators to suggest initialization.

```python
@dataclass
class ProjectHealth:
    """Track various project health metrics."""

    lint_error_trend: list[int]  # Errors over time
    test_coverage_trend: list[float]  # Coverage over time
    dependency_age: dict[str, int]  # Days since last update
    config_completeness: float  # 0-1 score

    def needs_init(self) -> bool:
        """Determine if project health suggests init needed."""
        # Rising lint errors
        if self._is_trending_up(self.lint_error_trend):
            return True

        # Declining test coverage
        if self._is_trending_down(self.test_coverage_trend):
            return True

        # Very old dependencies
        if any(age > 180 for age in self.dependency_age.values()):
            return True

        # Incomplete configuration
        if self.config_completeness < 0.8:
            return True

        return False
```

**Benefits**:

- Proactive quality maintenance
- Data-driven decisions
- Trend-based interventions

## 🚀 Implementation Priority

1. **High Priority** (Most valuable, easiest to implement):

   - Tool version checks
   - Dependency security updates
   - Git hook integration

1. **Medium Priority** (Valuable but more complex):

   - Configuration drift detection
   - Smart scheduling
   - Project health metrics

1. **Low Priority** (Nice to have):

   - Complex heuristics
   - ML-based predictions
   - Team behavior learning

## 📝 Configuration Options

Add these to environment variables or `crackerjack.toml`:

```toml
[tool.crackerjack.auto_init]
# How often to check for updates
check_interval_days = 7

# Tool version checking
check_tool_updates = true
auto_update_tools = false  # Require confirmation

# Configuration integrity
verify_config_integrity = true
config_checksum_file = "~/.cache/crackerjack/config.hash"

# Smart scheduling
schedule_type = "weekly"  # weekly, commit-based, activity-based
schedule_day = "monday"
commit_threshold = 50

# Security monitoring
check_security_updates = true
security_check_interval_hours = 24

# Project health
track_health_metrics = true
health_threshold = 0.7
```

## 🎯 Next Steps

1. **Community Feedback**: Gather user preferences on auto-init behavior
1. **Gradual Rollout**: Implement features behind feature flags
1. **Telemetry**: Anonymous usage data to improve heuristics
1. **Integration**: Work with popular CI/CD platforms
1. **Documentation**: Comprehensive guides for each feature

## 🖥️ MCP Server Clustering

Scale MCP server capacity for enterprise deployments with automatic load balancing.

```python
class MCPCluster:
    """Manage a cluster of MCP servers for high availability."""

    def __init__(self, cluster_config: dict):
        self.nodes = []
        self.load_balancer = LoadBalancer()
        self.health_monitor = HealthMonitor()

    async def add_node(self, host: str, port: int):
        """Add a new MCP server node to the cluster."""
        node = MCPNode(host, port)
        await node.start()

        if await self._health_check(node):
            self.nodes.append(node)
            self.load_balancer.register_node(node)

    async def route_request(self, request: MCPRequest) -> MCPResponse:
        """Route request to optimal node."""
        # Consider factors like:
        # - Current load
        # - Node specialization (e.g., some nodes for heavy processing)
        # - Geographic proximity
        # - Request type

        optimal_node = self.load_balancer.select_node(
            criteria={
                "load": request.estimated_complexity,
                "affinity": request.session_id,  # Session stickiness
                "capabilities": request.required_tools,
            }
        )

        return await optimal_node.execute(request)

    async def handle_node_failure(self, failed_node: MCPNode):
        """Gracefully handle node failures."""
        # Remove from rotation
        self.load_balancer.unregister_node(failed_node)

        # Redistribute active sessions
        active_sessions = failed_node.get_active_sessions()
        for session in active_sessions:
            backup_node = self.load_balancer.select_backup_node()
            await backup_node.restore_session(session)
```

**Benefits**:

- High availability for enterprise deployments
- Horizontal scaling for large development teams
- Automatic failover and recovery
- Session persistence across node failures

**Configuration**:

```yaml
# mcp-cluster.yaml
cluster:
  name: "crackerjack-production"
  nodes:
    - host: "mcp-1.company.com"
      port: 8000
      specialization: ["quality", "testing"]
    - host: "mcp-2.company.com"
      port: 8000
      specialization: ["security", "dependencies"]

  load_balancer:
    algorithm: "weighted_round_robin"
    health_check_interval: 30
    session_stickiness: true

  monitoring:
    metrics_endpoint: "http://metrics.company.com/mcp"
    alert_thresholds:
      response_time_p95: 5000  # ms
      error_rate: 0.05  # 5%
```

## 📚 Documentation Generation with MkDocs Material

Add comprehensive documentation generation capabilities using MkDocs Material theme, following the excellent patterns from FastAPI, Starlette, and Typer documentation.

### Implementation Strategy

````python
class DocsGenerator:
    """Generate beautiful documentation using MkDocs Material with AI-enhanced virtual docstrings."""

    def __init__(self, project_path: Path):
        self.project_path = project_path
        self.docs_path = project_path / "docs"
        self.mkdocs_config = project_path / "mkdocs.yml"
        self.virtual_docs_path = project_path / ".cache" / "virtual_docs"
        self.ai_client = self._initialize_ai_client()

    def setup_mkdocs_material(self):
        """Initialize MkDocs Material configuration for docstring-free projects."""
        config = {
            "site_name": self._get_project_name(),
            "site_description": self._get_project_description(),
            "theme": {
                "name": "material",
                "features": [
                    "navigation.tabs",
                    "navigation.sections",
                    "navigation.expand",
                    "navigation.top",
                    "search.highlight",
                    "search.share",
                    "content.code.copy",
                    "content.code.annotate",
                ],
                "palette": [
                    {
                        "scheme": "default",
                        "primary": "blue",
                        "accent": "blue",
                        "toggle": {
                            "icon": "material/brightness-7",
                            "name": "Switch to dark mode",
                        },
                    },
                    {
                        "scheme": "slate",
                        "primary": "blue",
                        "accent": "blue",
                        "toggle": {
                            "icon": "material/brightness-4",
                            "name": "Switch to light mode",
                        },
                    },
                ],
            },
            "markdown_extensions": [
                "pymdownx.highlight",
                "pymdownx.superfences",
                "pymdownx.tabbed",
                "pymdownx.snippets",
                "admonition",
                "pymdownx.details",
            ],
            "plugins": ["search", "mkdocstrings", "git-revision-date-localized"],
            # Point mkdocstrings to virtual docs with AI-generated docstrings
            "watch": [str(self.virtual_docs_path)],
        }

        with open(self.mkdocs_config, "w") as f:
            yaml.dump(config, f, default_flow_style=False)

    async def generate_virtual_docstring_files(self):
        """Create temporary Python files with AI-generated docstrings for pydoc-markdown."""
        self.virtual_docs_path.mkdir(parents=True, exist_ok=True)

        # Find all Python files (excluding tests)
        source_files = list(self.project_path.rglob("*.py"))
        source_files = [
            f
            for f in source_files
            if not any(
                part.startswith(("test_", "__pycache__", ".")) for part in f.parts
            )
        ]

        for source_file in source_files:
            virtual_file = await self._create_virtual_docstring_file(source_file)

            # Validate against existing markdown docs for accuracy
            await self._validate_against_markdown_docs(virtual_file, source_file)

    async def _create_virtual_docstring_file(self, source_file: Path) -> Path:
        """Create a virtual Python file with AI-generated docstrings."""
        # Read the original source code
        source_code = source_file.read_text()

        # Parse AST to identify functions, classes, methods
        tree = ast.parse(source_code)

        # Extract context from existing markdown docs
        existing_docs_context = self._extract_markdown_context(source_file)

        # Generate AI docstrings using existing docs as reference
        enhanced_code = await self._add_ai_docstrings(
            source_code, tree, existing_docs_context, source_file
        )

        # Create virtual file in temp location
        relative_path = source_file.relative_to(self.project_path)
        virtual_file = self.virtual_docs_path / relative_path
        virtual_file.parent.mkdir(parents=True, exist_ok=True)
        virtual_file.write_text(enhanced_code)

        return virtual_file

    def _extract_markdown_context(self, source_file: Path) -> dict[str, str]:
        """Extract relevant context from existing markdown documentation."""
        context = {}

        # Check README.md for usage examples
        readme = self.project_path / "README.md"
        if readme.exists():
            readme_content = readme.read_text()
            context["readme"] = readme_content

            # Extract code examples that reference this file
            context["examples"] = self._extract_code_examples_for_file(
                readme_content, source_file
            )

        # Check docs/ folder for related documentation
        if self.docs_path.exists():
            for doc_file in self.docs_path.rglob("*.md"):
                doc_content = doc_file.read_text()
                if self._is_doc_relevant_to_file(doc_content, source_file):
                    context[str(doc_file.name)] = doc_content

        return context

    async def _add_ai_docstrings(
        self,
        source_code: str,
        tree: ast.AST,
        docs_context: dict[str, str],
        source_file: Path,
    ) -> str:
        """Use AI to generate accurate docstrings based on existing documentation."""

        prompt = f"""
        Generate comprehensive docstrings for this Python code, following these requirements:

        1. **Accuracy Requirement**: Base docstrings on the provided documentation context
        2. **No Docstring Preference**: The original code intentionally has no docstrings - you're creating them ONLY for documentation generation
        3. **Content Consistency**: Ensure generated docstrings match the tone and accuracy of existing markdown docs
        4. **API Documentation**: Focus on public APIs, parameters, return types, and usage examples

        Existing Documentation Context:
        {json.dumps(docs_context, indent=2)}

        Source File: {source_file}

        Python Code:
        ```python
        {source_code}
        ```

        Rules:
        - Generate docstrings in Google style format
        - Include type information in docstrings (Args, Returns sections)
        - Reference examples from existing docs where applicable
        - For public APIs, include usage examples from README/docs
        - Maintain accuracy with existing documentation claims
        - Don't contradict information in markdown docs
        - Focus on user-facing functionality over implementation details

        Return the complete Python code with added docstrings.
        """

        # Use AI client (Claude, GPT, etc.) to generate enhanced code
        enhanced_code = await self.ai_client.generate_docstrings(prompt)

        return enhanced_code

    async def _validate_against_markdown_docs(
        self, virtual_file: Path, source_file: Path
    ):
        """Ensure generated docstrings don't contradict existing markdown documentation."""

        # Read the generated docstrings
        virtual_content = virtual_file.read_text()

        # Extract claims from docstrings
        docstring_claims = self._extract_docstring_claims(virtual_content)

        # Check against existing markdown docs
        markdown_claims = self._extract_markdown_claims()

        # Identify any conflicts
        conflicts = self._find_conflicts(docstring_claims, markdown_claims)

        if conflicts:
            # Re-generate with conflict resolution
            await self._resolve_documentation_conflicts(
                virtual_file, conflicts, markdown_claims
            )

    def _extract_docstring_claims(self, code: str) -> list[dict]:
        """Extract factual claims from generated docstrings."""
        # Parse docstrings and extract:
        # - Parameter types and descriptions
        # - Return value descriptions
        # - Usage examples
        # - Feature descriptions
        pass

    def _extract_markdown_claims(self) -> list[dict]:
        """Extract factual claims from existing markdown documentation."""
        # Parse README.md and docs/ files to extract:
        # - Feature descriptions
        # - API usage examples
        # - Configuration options
        # - Command-line interface descriptions
        pass

    async def _resolve_documentation_conflicts(
        self,
        virtual_file: Path,
        conflicts: list[dict],
        authoritative_claims: list[dict],
    ):
        """Regenerate docstrings to resolve conflicts with authoritative markdown docs."""

        conflict_resolution_prompt = f"""
        The generated docstrings conflict with the authoritative markdown documentation.
        Please regenerate the docstrings ensuring they align with the markdown docs.

        Conflicts found:
        {json.dumps(conflicts, indent=2)}

        Authoritative documentation claims:
        {json.dumps(authoritative_claims, indent=2)}

        Regenerate the Python file with corrected docstrings that match the markdown documentation.
        """

        corrected_code = await self.ai_client.resolve_conflicts(
            conflict_resolution_prompt
        )
        virtual_file.write_text(corrected_code)
````

### Pre-commit Hook Integration

```python
# Add to dynamic_config.py HOOKS_REGISTRY
(
    {
        "id": "ai-docs-generate",
        "name": "ai-docs-generate",
        "repo": "local",
        "rev": "",
        "tier": 2,
        "time_estimate": 15.0,  # AI generation takes longer
        "stages": ["pre-push", "manual"],
        "args": ["--generate-virtual-docstrings"],
        "files": "^(src/|crackerjack/.*\\.py)$",
        "exclude": "__pycache__|test_|virtual_docs/",
        "additional_dependencies": [
            "openai>=1.0.0",  # or anthropic for Claude
            "tiktoken>=0.5.0",
        ],
        "types_or": ["python"],
        "language": "python",
        "entry": "python -m crackerjack._internal.ai_docs_generator",
        "experimental": False,
    },
)
(
    {
        "id": "mkdocs-build-virtual",
        "name": "mkdocs-build-virtual",
        "repo": "local",
        "rev": "",
        "tier": 2,
        "time_estimate": 3.0,
        "stages": ["pre-push", "manual"],
        "args": ["--config-file", "mkdocs.yml", "--strict"],
        "files": "^(docs/|mkdocs\\.yml|README\\.md|\\.cache/virtual_docs/)",
        "exclude": None,
        "additional_dependencies": [
            "mkdocs-material>=9.0.0",
            "mkdocstrings[python]>=0.20.0",
            "mkdocs-git-revision-date-localized-plugin",
        ],
        "types_or": None,
        "language": "python",
        "entry": "mkdocs build",
        "experimental": False,
    },
)
{
    "id": "docs-accuracy-check",
    "name": "docs-accuracy-check",
    "repo": "local",
    "rev": "",
    "tier": 2,
    "time_estimate": 5.0,
    "stages": ["pre-push", "manual"],
    "args": ["--validate-consistency"],
    "files": "^(docs/|README\\.md|\\.cache/virtual_docs/)",
    "exclude": "__pycache__|test_",
    "additional_dependencies": ["difflib"],
    "types_or": None,
    "language": "python",
    "entry": "python -m crackerjack._internal.docs_validator",
    "experimental": False,
}
```

### AI-Enhanced Documentation Workflow

```python
# crackerjack/_internal/ai_docs_generator.py
class AIDocsGenerator:
    """Generate virtual docstring files using AI while preserving no-docstring codebase."""

    def __init__(self, project_path: Path):
        self.project_path = project_path
        self.virtual_docs_path = project_path / ".cache" / "virtual_docs"
        self.docs_cache_path = project_path / ".cache" / "docs_cache.json"

    async def generate_virtual_docs(self):
        """Main entry point for generating virtual documentation files."""
        # Clean previous virtual docs
        if self.virtual_docs_path.exists():
            shutil.rmtree(self.virtual_docs_path)

        # Regenerate from current source
        await self.generate_virtual_docstring_files()

        # Validate against markdown docs
        await self.validate_documentation_consistency()

        print("✅ Virtual documentation files generated successfully")

    async def validate_documentation_consistency(self):
        """Ensure generated docs match authoritative markdown sources."""

        inconsistencies = []

        # Compare API claims between virtual docstrings and README
        readme_claims = self._extract_readme_api_claims()
        virtual_claims = self._extract_virtual_api_claims()

        for claim_type in ["parameters", "return_types", "usage_examples"]:
            readme_items = readme_claims.get(claim_type, {})
            virtual_items = virtual_claims.get(claim_type, {})

            conflicts = self._find_claim_conflicts(readme_items, virtual_items)
            if conflicts:
                inconsistencies.extend(conflicts)

        if inconsistencies:
            self._report_inconsistencies(inconsistencies)
            raise ValueError("Documentation consistency check failed")

        print("✅ Documentation consistency validated")

    def _find_claim_conflicts(self, authoritative: dict, generated: dict) -> list[dict]:
        """Find conflicts between authoritative docs and generated docstrings."""
        conflicts = []

        for function_name, auth_claim in authoritative.items():
            gen_claim = generated.get(function_name)
            if gen_claim and auth_claim != gen_claim:
                conflicts.append(
                    {
                        "function": function_name,
                        "authoritative": auth_claim,
                        "generated": gen_claim,
                        "conflict_type": "claim_mismatch",
                    }
                )

        return conflicts
```

### Automatic Documentation Structure

```python
def generate_docs_structure(self):
    """Create comprehensive docs structure like FastAPI/Typer."""

    docs_structure = {
        "index.md": self._generate_index_page(),
        "installation.md": self._generate_installation_guide(),
        "tutorial/": {
            "index.md": self._generate_tutorial_index(),
            "first-steps.md": self._generate_first_steps(),
            "configuration.md": self._generate_config_guide(),
            "autofix.md": self._generate_autofix_guide(),
            "enterprise.md": self._generate_enterprise_guide(),
        },
        "reference/": {
            "api.md": self._generate_api_reference(),
            "cli.md": self._generate_cli_reference(),
            "hooks.md": self._generate_hooks_reference(),
        },
        "advanced/": {
            "mcp-integration.md": self._generate_mcp_guide(),
            "custom-hooks.md": self._generate_custom_hooks(),
            "ci-cd.md": self._generate_cicd_guide(),
        },
        "contributing.md": self._generate_contributing_guide(),
        "changelog.md": self._generate_changelog(),
    }

    self._create_docs_files(docs_structure)


def _generate_api_reference(self) -> str:
    """Generate API reference from docstrings."""
    return """
# API Reference

::: crackerjack.create_crackerjack_runner
    options:
      show_root_heading: true
      show_source: false

::: crackerjack.Options
    options:
      show_root_heading: true
      members_order: source

::: crackerjack.Crackerjack
    options:
      show_root_heading: true
      members:
        - process
        - clean_code
        - run_tests
    """
```

### Documentation Features

**Automated Features:**

- **API Documentation**: Auto-generated from docstrings using mkdocstrings
- **CLI Reference**: Auto-generated command documentation
- **Code Examples**: Extracted from tests and README
- **Changelog**: Auto-generated from git commits and releases
- **Installation Guide**: Platform-specific instructions
- **Tutorial**: Step-by-step guides like FastAPI docs

**Enhanced Navigation:**

- **Search**: Full-text search with highlighting
- **Dark/Light Mode**: Automatic theme switching
- **Mobile-Friendly**: Responsive design for all devices
- **Code Copy**: One-click copying of code examples
- **Git Integration**: Last updated timestamps

### Integration with Existing Workflow

```bash
# New CLI options for documentation
python -m crackerjack --docs          # Generate and serve docs locally
python -m crackerjack --docs-build    # Build docs for deployment
python -m crackerjack --docs-deploy   # Deploy to GitHub Pages

# Pre-commit hooks ensure docs stay current
# Automatically rebuilds docs when:
# - API changes (docstrings modified)
# - README.md updated
# - New examples added
# - Configuration changes
```

### Benefits

**Developer Experience:**

- **Professional Documentation**: Match quality of FastAPI/Starlette docs
- **No-Docstring Codebase**: Respects preference for clean, docstring-free code
- **AI-Enhanced Accuracy**: Generated docstrings validated against authoritative markdown docs
- **Zero Configuration**: Works out-of-the-box with sensible defaults
- **Auto-Generated**: Stays current with code changes via virtual files
- **Interactive**: Code examples with syntax highlighting
- **Searchable**: Full-text search across all documentation

**Documentation Integrity:**

- **Consistency Validation**: Ensures virtual docstrings match existing markdown documentation
- **Authoritative Source**: Markdown docs remain the single source of truth
- **Conflict Resolution**: AI automatically resolves inconsistencies with markdown docs
- **Accuracy Checks**: Pre-commit hooks validate documentation consistency

**Project Quality:**

- **Increased Adoption**: Professional docs attract users while preserving clean codebase
- **Reduced Support**: Self-service documentation without polluting source code
- **Better Onboarding**: Clear tutorials and examples
- **API Discoverability**: Complete reference documentation from AI-generated virtual files

**Unique Advantages:**

- **Best of Both Worlds**: Professional API docs without source code docstrings
- **Markdown-First**: Existing markdown documentation drives AI docstring generation
- **Temporary Generation**: Virtual docstring files exist only during documentation build
- **Source Code Purity**: No changes to actual codebase, maintains clean architecture

This would position crackerjack projects with documentation quality matching the best Python packages like FastAPI, Starlette, and Typer, while respecting the no-docstring philosophy!

## 🖥️ Enhanced Progress Monitor with Textual TUI

Add interactive Textual-based interface for advanced job monitoring and analysis.

### Core Interactive Features

```python
class CrackerjackTUI(App):
    """Interactive Textual TUI for enhanced monitoring capabilities."""

    CSS_PATH = "crackerjack_tui.css"
    TITLE = "Crackerjack Progress Monitor"
    BINDINGS = [
        ("q", "quit", "Quit"),
        ("r", "refresh", "Manual Refresh"),
        ("h", "show_history", "Job History"),
        ("l", "show_logs", "Live Logs"),
        ("f", "filter_jobs", "Filter Jobs"),
        ("e", "export_data", "Export Data"),
        ("ctrl+c", "force_quit", "Force Quit"),
    ]

    def compose(self) -> ComposeResult:
        yield Header()
        with Horizontal():
            # Left sidebar: Job list and controls
            with Vertical(classes="sidebar"):
                yield Static("Active Jobs", classes="section-header")
                yield DataTable(id="jobs-table")
                yield Static("Controls", classes="section-header")
                yield Button("Pause Monitoring", id="pause-btn")
                yield Button("Export Progress", id="export-btn")
                yield Button("View History", id="history-btn")

            # Main content: Selected job details
            with Vertical(classes="main-content"):
                yield Static("Job Details", classes="section-header")
                yield Container(id="job-details")
                yield Static("Live Logs", classes="section-header")
                yield Container(id="log-viewer", classes="scrollable")

        # Bottom status bar
        yield Footer()

    async def on_mount(self):
        """Initialize TUI components."""
        self.monitor = MultiJobProgressMonitor()
        self.selected_job_id = None
        self.paused = False

        # Setup periodic refresh
        self.set_interval(1.0, self.update_display)

        # Setup job table
        table = self.query_one("#jobs-table", DataTable)
        table.add_columns("Project", "Progress", "Stage", "Status", "Time")

    async def update_display(self):
        """Update all TUI components."""
        if self.paused:
            return

        # Discover and update jobs
        await self.monitor.discover_jobs()

        # Update jobs table
        await self.update_jobs_table()

        # Update selected job details
        if self.selected_job_id and self.selected_job_id in self.monitor.jobs:
            await self.update_job_details(self.selected_job_id)
```

### Job History and Analysis

```python
class JobHistoryViewer:
    """View and analyze completed job history."""

    def __init__(self, storage_path: Path):
        self.storage = JobStorage(storage_path)
        self.analytics = JobAnalytics()

    def create_history_table(self) -> DataTable:
        """Create table showing job history with filtering."""
        table = DataTable()
        table.add_columns(
            "Date",
            "Project",
            "Duration",
            "Iterations",
            "Fixes",
            "Errors",
            "Status",
            "Actions",
        )

        # Load historical jobs
        historical_jobs = self.storage.load_completed_jobs(limit=100)

        for job in historical_jobs:
            table.add_row(
                job.completed_at.strftime("%Y-%m-%d %H:%M"),
                job.project_name,
                f"{job.total_duration:.1f}s",
                f"{job.final_iteration}/{job.max_iterations}",
                str(job.total_fixes),
                str(job.total_errors),
                job.final_status,
                "View Details",
            )

        return table

    async def show_job_analytics(self) -> Container:
        """Show analytics and trends for job performance."""
        container = Container()

        # Success rate over time
        success_rate = self.analytics.calculate_success_rate_trend()
        container.mount(self.create_success_chart(success_rate))

        # Average completion time by project
        avg_times = self.analytics.calculate_avg_completion_times()
        container.mount(self.create_timing_chart(avg_times))

        # Most common error patterns
        error_patterns = self.analytics.analyze_error_patterns()
        container.mount(self.create_error_analysis(error_patterns))

        return container
```

### Real-time Log Streaming

```python
class LogViewer:
    """Real-time log streaming and filtering."""

    def __init__(self, job_id: str):
        self.job_id = job_id
        self.log_buffer = []
        self.filters = {
            "level": "ALL",  # DEBUG, INFO, WARNING, ERROR, ALL
            "component": "ALL",  # hooks, tests, autofix, ALL
            "search": "",
        }

    async def stream_logs(self) -> AsyncIterator[str]:
        """Stream live logs from job execution."""
        log_file = Path(f"/tmp/crackerjack-{self.job_id}.log")

        if not log_file.exists():
            yield "[dim]No logs available yet...[/dim]"
            return

        # Follow log file like `tail -f`
        async with aiofiles.open(log_file, "r") as f:
            # Seek to end for live streaming
            await f.seek(0, 2)

            while True:
                line = await f.readline()
                if line:
                    if self.should_show_line(line):
                        yield self.format_log_line(line)
                else:
                    await asyncio.sleep(0.1)

    def should_show_line(self, line: str) -> bool:
        """Apply filters to determine if line should be shown."""
        # Level filtering
        if self.filters["level"] != "ALL":
            if not self.filters["level"].lower() in line.lower():
                return False

        # Component filtering
        if self.filters["component"] != "ALL":
            if not self.filters["component"].lower() in line.lower():
                return False

        # Search filtering
        if self.filters["search"]:
            if self.filters["search"].lower() not in line.lower():
                return False

        return True

    def format_log_line(self, line: str) -> str:
        """Format log line with syntax highlighting."""
        # Add colors based on log level
        if "ERROR" in line:
            return f"[red]{line.strip()}[/red]"
        elif "WARNING" in line:
            return f"[yellow]{line.strip()}[/yellow]"
        elif "INFO" in line:
            return f"[blue]{line.strip()}[/blue]"
        elif "DEBUG" in line:
            return f"[dim]{line.strip()}[/dim]"
        else:
            return line.strip()
```

### Interactive Job Control

```python
class JobController:
    """Interactive job control and manipulation."""

    def __init__(self, websocket_url: str):
        self.websocket_url = websocket_url
        self.client = None

    async def pause_job(self, job_id: str) -> bool:
        """Send pause signal to running job."""
        if not self.client:
            await self.connect()

        message = {"action": "pause_job", "job_id": job_id, "timestamp": time.time()}

        await self.client.send(json.dumps(message))
        response = await self.client.recv()

        return json.loads(response).get("success", False)

    async def resume_job(self, job_id: str) -> bool:
        """Send resume signal to paused job."""
        message = {"action": "resume_job", "job_id": job_id, "timestamp": time.time()}

        await self.client.send(json.dumps(message))
        response = await self.client.recv()

        return json.loads(response).get("success", False)

    async def restart_failed_job(self, job_id: str) -> str:
        """Restart a failed job with same parameters."""
        job_config = await self.get_job_config(job_id)

        message = {
            "action": "restart_job",
            "original_job_id": job_id,
            "config": job_config,
            "timestamp": time.time(),
        }

        await self.client.send(json.dumps(message))
        response = await self.client.recv()

        return json.loads(response).get("new_job_id")
```

### Data Export and Reporting

```python
class ProgressReporter:
    """Export job progress data in various formats."""

    def __init__(self, jobs: dict[str, JobMetrics]):
        self.jobs = jobs

    def export_csv(self, output_path: Path) -> None:
        """Export current job status to CSV."""
        import csv

        with open(output_path, "w", newline="") as csvfile:
            writer = csv.writer(csvfile)
            writer.writerow(
                [
                    "Job ID",
                    "Project",
                    "Status",
                    "Progress %",
                    "Current Stage",
                    "Iteration",
                    "Elapsed Time",
                    "Fixes Applied",
                    "Errors Found",
                    "Started At",
                ]
            )

            for job in self.jobs.values():
                writer.writerow(
                    [
                        job.job_id,
                        job.project_name,
                        job.status,
                        job.overall_progress,
                        job.current_stage,
                        f"{job.iteration}/{job.max_iterations}",
                        f"{job.elapsed_time:.1f}s",
                        job.fixes_applied,
                        job.errors_count["total"],
                        datetime.fromtimestamp(job.start_time).isoformat(),
                    ]
                )

    def export_json(self, output_path: Path) -> None:
        """Export detailed job data as JSON."""
        export_data = {
            "export_timestamp": datetime.now().isoformat(),
            "total_jobs": len(self.jobs),
            "jobs": [],
        }

        for job in self.jobs.values():
            job_data = {
                "job_id": job.job_id,
                "project_name": job.project_name,
                "project_path": job.project_path,
                "status": job.status,
                "progress": {
                    "overall_progress": job.overall_progress,
                    "iteration": job.iteration,
                    "max_iterations": job.max_iterations,
                    "current_stage": job.current_stage,
                },
                "metrics": {
                    "fixes_applied": job.fixes_applied,
                    "errors_count": job.errors_count,
                    "failures_count": job.failures_count,
                },
                "timing": {
                    "start_time": job.start_time,
                    "elapsed_time": job.elapsed_time,
                    "estimated_completion": job.estimated_completion,
                },
                "stages": {
                    "results": job.stage_results,
                    "progress": job.stage_progress,
                    "durations": job.stage_durations,
                },
            }
            export_data["jobs"].append(job_data)

        with open(output_path, "w") as f:
            json.dump(export_data, f, indent=2)

    def generate_html_report(self, output_path: Path) -> None:
        """Generate comprehensive HTML report."""
        html_template = """
        <!DOCTYPE html>
        <html>
        <head>
            <title>Crackerjack Progress Report</title>
            <style>
                body { font-family: Arial, sans-serif; margin: 20px; }
                .job-card { border: 1px solid #ccc; margin: 10px; padding: 15px; border-radius: 5px; }
                .status-completed { border-left: 5px solid #28a745; }
                .status-failed { border-left: 5px solid #dc3545; }
                .status-running { border-left: 5px solid #007bff; }
                .progress-bar { width: 100%; height: 20px; background: #f0f0f0; border-radius: 10px; }
                .progress-fill { height: 100%; background: #007bff; border-radius: 10px; }
                .metrics { display: flex; gap: 20px; margin: 10px 0; }
                .metric { text-align: center; }
            </style>
        </head>
        <body>
            <h1>🚀 Crackerjack Progress Report</h1>
            <p>Generated: {timestamp}</p>
            <p>Total Jobs: {total_jobs}</p>

            {job_cards}
        </body>
        </html>
        """

        job_cards = ""
        for job in self.jobs.values():
            job_cards += self.generate_job_card_html(job)

        html_content = html_template.format(
            timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            total_jobs=len(self.jobs),
            job_cards=job_cards,
        )

        with open(output_path, "w") as f:
            f.write(html_content)
```

### Enhanced CLI Integration

```bash
# New TUI mode for advanced monitoring
python -m crackerjack --monitor --tui          # Interactive Textual interface
python -m crackerjack --monitor --export csv   # Export current jobs to CSV
python -m crackerjack --monitor --history      # Show job history
python -m crackerjack --monitor --analytics    # Job performance analytics

# Job control commands
python -m crackerjack --job-control pause <job_id>      # Pause specific job
python -m crackerjack --job-control resume <job_id>     # Resume paused job
python -m crackerjack --job-control restart <job_id>    # Restart failed job
python -m crackerjack --job-control logs <job_id>       # Stream job logs
```

### Benefits of Textual TUI

**Advanced Interactivity:**

- **Keyboard Navigation**: Efficient job browsing and control
- **Mouse Support**: Click to select jobs, scroll logs, resize panes
- **Real-time Updates**: Live refresh without clearing terminal history
- **Multi-pane Layout**: View multiple data sources simultaneously

**Enhanced Monitoring:**

- **Job History**: Review past executions and trends
- **Live Log Streaming**: Real-time error analysis and debugging
- **Interactive Filtering**: Focus on specific error types or components
- **Progress Analytics**: Identify performance patterns and bottlenecks

**Professional Workflow:**

- **Export Capabilities**: CSV, JSON, HTML reports for sharing
- **Job Control**: Pause, resume, restart operations
- **Session Persistence**: Maintain view state across terminal sessions
- **Customizable Views**: Filter and organize based on preferences

**Development Integration:**

- **Terminal Compatibility**: Works in tmux, screen, and modern terminals
- **CI/CD Integration**: Export data for build pipeline analysis
- **Team Collaboration**: Shareable progress reports and analytics
- **Debugging Support**: Detailed log analysis and error pattern detection

This would provide a comprehensive monitoring solution while maintaining the current Rich Live interface as the lightweight default option!

These enhancements would make crackerjack even more intelligent about maintaining project quality without being intrusive!
