# Extended Artifacts Implementation

## Summary

This implementation adds support for **5 new artifact types** that were previously only available in the bash/CLI version of spec-kit (GitHub), bringing feature parity between the Python library and the CLI tool.

## New Artifacts Added

### 1. DataModel (`data-model.md`)
**Purpose**: Define database schema and data structures

**Models**:
- `DataModel` - Complete data model specification
- `DataModelEntity` - Entity/table definition
- `DataModelField` - Field/attribute with type, constraints, relationships

**File Location**: `specs/{feature-id}/data-model.md`

**Use Cases**:
- Features requiring database persistence
- API design with clear data contracts
- Migration generation
- ORM schema definition

### 2. ResearchFindings (`research.md`)
**Purpose**: Document technology decisions and architectural choices

**Models**:
- `ResearchFindings` - Complete research documentation
- `TechnologyDecision` - Decision with rationale and alternatives

**File Location**: `specs/{feature-id}/research.md`

**Use Cases**:
- Technology stack selection justification
- Architectural decision records (ADR)
- Trade-off analysis
- Implementation pattern documentation

### 3. APIContract (`contracts/api.md`)
**Purpose**: Formal API specification

**Models**:
- `APIContract` - Complete API specification
- `APIEndpoint` - Individual endpoint with request/response schemas

**File Location**: `specs/{feature-id}/contracts/api.md`

**Use Cases**:
- Frontend/Backend contract definition
- API documentation generation
- OpenAPI/Swagger spec foundation
- Client SDK generation
- Contract testing

### 4. QualityChecklist (`checklists/requirements.md`)
**Purpose**: Specification quality validation

**Models**:
- `QualityChecklist` - Complete validation checklist
- `ChecklistItem` - Individual check with pass/fail status

**File Location**: `specs/{feature-id}/checklists/requirements.md`

**Use Cases**:
- Specification quality gates
- Pre-implementation validation
- Stakeholder sign-off tracking
- Completeness verification

### 5. QuickstartGuide (`quickstart.md`)
**Purpose**: Getting started documentation

**Models**:
- `QuickstartGuide` - Complete quickstart guide with installation, configuration, examples

**File Location**: `specs/{feature-id}/quickstart.md`

**Use Cases**:
- Developer onboarding
- Feature setup documentation
- Usage examples
- Troubleshooting guide

## Files Modified

### 1. `src/speckit/schemas.py`
- Added 10 new Pydantic models (486 lines):
  - `DataModelField`
  - `DataModelEntity`
  - `DataModel`
  - `TechnologyDecision`
  - `ResearchFindings`
  - `APIEndpoint`
  - `APIContract`
  - `ChecklistItem`
  - `QualityChecklist`
  - `QuickstartGuide`

All models include:
- Pydantic validation
- `to_markdown()` export method
- Proper typing with `Optional` and `Field` defaults
- ISO datetime handling

### 2. `src/speckit/storage/file_storage.py`
- Added 5 file path constants
- Added 5 new save methods:
  - `save_data_model()`
  - `save_research()`
  - `save_api_contract()` (creates `contracts/` subdir)
  - `save_checklist()` (creates `checklists/` subdir)
  - `save_quickstart()`
- Updated `get_artifact_path()` to support new artifact types
- Maintains backup functionality for all new artifacts

### 3. `src/speckit/__init__.py`
- Exported all new models in `__all__`
- Added imports for new schemas

## Feature Parity Comparison

| Artifact | Bash/CLI Version | Python Library (Before) | Python Library (After) |
|----------|------------------|-------------------------|------------------------|
| `spec.md` | ✅ | ✅ | ✅ |
| `plan.md` | ✅ | ✅ | ✅ |
| `tasks.md` | ✅ | ✅ | ✅ |
| `constitution.md` | ✅ | ✅ | ✅ |
| `data-model.md` | ✅ | ❌ | ✅ **NEW** |
| `research.md` | ✅ | ❌ | ✅ **NEW** |
| `contracts/api.md` | ✅ | ❌ | ✅ **NEW** |
| `checklists/requirements.md` | ✅ | ❌ | ✅ **NEW** |
| `quickstart.md` | ✅ | ❌ | ✅ **NEW** |

**Status**: ✅ **FEATURE PARITY ACHIEVED** for artifact storage

## Usage Examples

### DataModel
```python
from speckit import DataModel, DataModelEntity, DataModelField

# Create data model
data_model = DataModel(
    feature_id="001-user-auth",
    feature_name="User Authentication",
    overview="Database schema for user authentication system",
    database_type="PostgreSQL",
    orm_framework="Prisma",
    entities=[
        DataModelEntity(
            name="User",
            description="User account with authentication credentials",
            fields=[
                DataModelField(
                    name="id",
                    field_type="UUID",
                    description="Unique user identifier",
                    is_primary_key=True
                ),
                DataModelField(
                    name="email",
                    field_type="String",
                    description="User email address",
                    constraints=["unique", "indexed"]
                ),
                DataModelField(
                    name="password_hash",
                    field_type="String",
                    description="Bcrypt password hash"
                )
            ],
            relationships=["User (1) → (N) Sessions"]
        )
    ]
)

# Save to file
kit = SpecKit("./my-project")
kit.storage.save_data_model(data_model, "001-user-auth")
# Creates: specs/001-user-auth/data-model.md
```

### APIContract
```python
from speckit import APIContract, APIEndpoint

contract = APIContract(
    feature_id="001-user-auth",
    feature_name="User Authentication",
    base_url="/api/v2",
    endpoints=[
        APIEndpoint(
            method="POST",
            path="/auth/register",
            summary="Register new user",
            request_body={
                "email": "string",
                "password": "string"
            },
            response_schema={
                "user_id": "uuid",
                "access_token": "string"
            },
            error_responses={
                "400": "Invalid input",
                "409": "Email already exists"
            }
        )
    ]
)

kit.storage.save_api_contract(contract, "001-user-auth")
# Creates: specs/001-user-auth/contracts/api.md
```

### QualityChecklist
```python
from speckit import QualityChecklist, ChecklistItem

checklist = QualityChecklist(
    feature_id="001-user-auth",
    feature_name="User Authentication",
    content_quality=[
        ChecklistItem(
            id="CQ-001",
            criterion="No implementation details in spec",
            status="PASS"
        )
    ],
    requirement_completeness=[
        ChecklistItem(
            id="RC-001",
            criterion="All requirements testable",
            status="PASS"
        )
    ],
    overall_status="PASS"
)

kit.storage.save_checklist(checklist, "001-user-auth")
# Creates: specs/001-user-auth/checklists/requirements.md
```

## Next Steps

### Phase 2: LLM Generation Methods (To be implemented)
Add methods to `SpecKit` class for AI-powered generation:

```python
class SpecKit:
    def generate_data_model(self, spec: Specification, plan: TechnicalPlan) -> DataModel:
        """Generate data model from spec and plan using LLM."""
        ...

    def generate_research(self, plan: TechnicalPlan) -> ResearchFindings:
        """Generate technology research from plan using LLM."""
        ...

    def generate_api_contract(self, spec: Specification, plan: TechnicalPlan) -> APIContract:
        """Generate API contract from spec and plan using LLM."""
        ...

    def generate_checklist(self, spec: Specification) -> QualityChecklist:
        """Generate quality checklist for specification using LLM."""
        ...

    def generate_quickstart(self, spec: Specification, plan: TechnicalPlan) -> QuickstartGuide:
        """Generate quickstart guide using LLM."""
        ...
```

### Phase 3: Integration with Velospec Platform
Update Velospec backend to use new artifacts:
- Add generation triggers after spec/plan phases
- Store artifacts in database
- Commit artifacts to Git repositories
- Display artifacts in UI

## Breaking Changes

None. This is a **purely additive change** - all existing functionality remains unchanged.

## Backward Compatibility

✅ **Fully backward compatible**
- Existing code continues to work without modifications
- New artifacts are optional
- No changes to existing methods or signatures

## Testing

### Manual Testing
All models have been:
- Syntax validated with `python -m py_compile`
- Type-checked structure verified
- Export methods (`to_markdown()`) implemented

### Recommended Tests (Future PR)
- Unit tests for each model's `to_markdown()` method
- Integration tests for FileStorage save methods
- End-to-end test for complete workflow with all artifacts

## References

- **Original Issue**: Comparison between bash and Python versions
- **Bash Version**: https://github.com/github/spec-kit
- **Python Fork**: https://github.com/suportly/spec-kit
- **Analysis Document**: `/home/alairjt/workspace/suportly/spec-platform/SPECKIT_BASH_VS_PYTHON_COMPARISON.md`
