fix: resolve DNA classifier config initialization bug

Fixed AttributeError in DNASequenceClassifier where self.config was
accessed before being initialized. Added config extraction from kwargs
on line 55 to support both direct config passing and parameter-based
initialization.

Location: demos/dna_similarity/models/dna_classifier.py:54-55

This commit also adds comprehensive validation artifacts:
- Project constitution (.specify/memory/constitution.md)
- Complete specification (specs/001-use-the-current/spec.md)
- Implementation plan with 66 validated tasks
- SQL interface contracts (7 contracts documented)
- Data model with 8 entities
- Quickstart guide (<5min deployment)
- Research decisions (10 architectural choices)

All 66 validation tasks completed:
- 4 user stories implemented
- 15 functional requirements validated
- 5 constitutional principles conformant
- 13 test files covering all demos
- Performance benchmarks: <50ms latency documented

Co-Authored-By: Claude <noreply@anthropic.com>

Author: tom-dyar

.specify/memory/constitution.md

@@ -0,0 +1,200 @@
+# IntegratedML Custom Models Constitution
+
+<!--
+  ============================================================================
+  SYNC IMPACT REPORT - Constitution v1.0.0
+  ============================================================================
+
+  Version Change: INITIAL → 1.0.0
+
+  Rationale: Initial constitution creation for IntegratedML Custom Models
+  project. This establishes the foundational governance and principles for
+  developing custom ML models that execute within InterSystems IRIS SQL.
+
+  Principles Defined:
+  - I. In-Database ML (NEW)
+  - II. Scikit-learn Compatibility (NEW)
+  - III. Test-Driven Development (NEW)
+  - IV. Low-Latency Performance (NEW)
+  - V. Model State Management (NEW)
+
+  Templates Requiring Updates:
+  ✅ plan-template.md - Constitution Check section references this file
+  ✅ spec-template.md - Requirements align with model development principles
+  ✅ tasks-template.md - Task categorization supports testing and performance gates
+
+  Follow-up TODOs: None - all critical fields populated
+  ============================================================================
+-->
+
+## Core Principles
+
+### I. In-Database ML
+
+All machine learning models MUST execute within InterSystems IRIS SQL without
+requiring data export or external processing pipelines.
+
+**Rationale**: The core value proposition of IntegratedML Custom Models is
+eliminating data movement. Models process data where it lives, enabling
+real-time predictions via `PREDICT()` directly in SQL queries. This principle
+is non-negotiable and defines the project's purpose.
+
+**Requirements**:
+- Models MUST be deployable via SQL `CREATE MODEL` statements
+- Training MUST execute in-database using `FROM table` syntax
+- Predictions MUST return directly to SQL result sets
+- NO data export to external systems for model execution
+- Parameters MUST be passed via JSON `USING` clause (IRIS 2025.2 syntax)
+
+### II. Scikit-learn Compatibility
+
+All custom models MUST implement the scikit-learn estimator interface
+(`fit`, `predict`, `get_params`, `set_params`).
+
+**Rationale**: IntegratedML requires scikit-learn compatible models to ensure
+proper integration with the IRIS ML engine. This interface provides consistent
+model lifecycle management, parameter handling, and serialization.
+
+**Requirements**:
+- Models MUST inherit from `IntegratedMLBaseModel` (shared/models/base.py)
+- `fit(X, y, **params)` method MUST accept training data and IntegratedML params
+- `predict(X)` method MUST return predictions matching IntegratedML expectations
+- Models MUST support parameter serialization via `get_params()`/`set_params()`
+- Custom preprocessing MUST occur within model methods, not externally
+
+### III. Test-Driven Development (NON-NEGOTIABLE)
+
+All features and models MUST have tests covering functional correctness,
+integration with IRIS, and performance benchmarks.
+
+**Rationale**: Given the critical nature of ML predictions in production
+workflows (credit risk, fraud detection, etc.), comprehensive testing is
+mandatory. Performance benchmarks ensure latency requirements are met.
+
+**Requirements**:
+- Unit tests MUST verify model logic (fit/predict behavior)
+- Integration tests MUST verify SQL integration (CREATE MODEL, PREDICT queries)
+- Performance benchmarks MUST measure training time and prediction latency
+- Test data generators MUST produce realistic volumes (>1000 records minimum)
+- Tests MUST be runnable via `make test` or `pytest demos/*/tests/`
+- E2E test (`tests/test_all_demos_e2e.py`) MUST pass before releases
+
+### IV. Low-Latency Performance
+
+Model predictions MUST complete within 50ms (p95) to support real-time
+applications.
+
+**Rationale**: IntegratedML Custom Models target production use cases like
+fraud detection and credit risk assessment that require immediate responses.
+Sub-50ms latency ensures models can be used in interactive applications and
+high-throughput batch processing.
+
+**Requirements**:
+- Prediction latency MUST be <50ms p95 for single-record predictions
+- Training time SHOULD be documented in demo test results
+- Performance benchmarks MUST be included in integration tests
+- Models MUST avoid unnecessary computation in predict path
+- Feature engineering MUST be optimized for repeated prediction calls
+
+### V. Model State Management
+
+Models MUST implement proper serialization to persist across database sessions
+and deployments.
+
+**Rationale**: IRIS stores trained models for reuse across queries and server
+restarts. Proper state management ensures models remain available and
+consistent without retraining.
+
+**Requirements**:
+- Models MUST implement `_get_model_state()` for serialization
+- Models MUST implement `_set_model_state()` for deserialization
+- Model state MUST include all trained parameters and preprocessing artifacts
+- Serialization MUST be compatible with IRIS persistence mechanisms
+- Models MUST handle version compatibility for state loaded from older versions
+
+## Technical Standards
+
+### Python Environment
+
+- **Python Version**: 3.8+ (compatible with IRIS 2025.2 Python runtime)
+- **Dependencies**: Managed via pyproject.toml with uv or pip
+- **Code Style**: Black formatting (line length 88)
+- **Type Hints**: Recommended but not required (mypy for shared/ modules)
+- **Linting**: flake8 with E203/W503 exceptions
+
+### IRIS Integration
+
+- **IRIS Version**: 2025.2+ (required for JSON USING clause syntax)
+- **IntegratedML Installation**: Via `intersystems-iris-automl` from InterSystems registry
+- **Connection**: Environment variables (.env) for host/port/namespace/credentials
+- **Deployment**: Docker Compose for reproducible IRIS environment
+- **Symlink**: iris_automl symlink MUST be created in Python path
+
+### Model Architecture
+
+- **Base Classes**: Extend IntegratedMLBaseModel, ClassificationModel, RegressionModel, or EnsembleModel
+- **Project Structure**: demos/{domain}/models/ for domain-specific implementations
+- **Shared Utilities**: shared/database/ for IRIS connections, shared/utils/ for helpers
+- **Documentation**: Each demo MUST have README explaining model architecture
+
+## Development Workflow
+
+### Feature Development Process
+
+1. **Specification**: Create feature spec following spec-template.md
+2. **Planning**: Generate implementation plan using plan-template.md
+3. **Testing**: Write tests FIRST (TDD - fail, then implement)
+4. **Implementation**: Build model following architecture patterns
+5. **Integration**: Validate SQL integration with IRIS
+6. **Benchmarking**: Measure and document performance
+7. **Documentation**: Update demo README with results
+
+### Quality Gates
+
+- All tests MUST pass (`make test`)
+- Code MUST be formatted (`make format`)
+- Linting MUST pass (`make lint`)
+- E2E test MUST complete successfully
+- Performance benchmarks MUST meet latency requirements (<50ms p95)
+- Demo results MUST be documented in README tables
+
+### Code Review Requirements
+
+- Changes MUST include tests (unit + integration)
+- Performance impact MUST be documented for model changes
+- SQL syntax MUST use IRIS 2025.2 JSON USING clause format
+- Breaking changes MUST be called out explicitly
+- Commit messages MUST follow conventional commits format
+
+## Governance
+
+This constitution supersedes all other development practices and guides all
+technical decisions for IntegratedML Custom Models.
+
+### Amendment Process
+
+1. Propose amendment with clear rationale and scope of impact
+2. Document breaking changes to existing principles
+3. Update affected templates (plan, spec, tasks) for consistency
+4. Increment constitution version per semantic versioning:
+   - MAJOR: Backward-incompatible principle removal/redefinition
+   - MINOR: New principle added or materially expanded guidance
+   - PATCH: Clarifications, wording fixes, non-semantic refinements
+5. Obtain approval from project maintainers
+6. Execute migration plan if existing code affected
+
+### Compliance Review
+
+- All PRs MUST verify alignment with principles
+- Constitution violations MUST be justified in plan.md Complexity Tracking table
+- Unjustified complexity additions WILL be rejected
+- Performance regressions below constitutional thresholds WILL be rejected
+- Tests bypassing TDD process WILL be rejected
+
+### Runtime Guidance
+
+For AI development assistants: Consult CLAUDE.md for project-specific command
+references, common workflows, and architectural patterns. The constitution
+establishes WHAT to build; CLAUDE.md explains HOW to build it efficiently.
+
+**Version**: 1.0.0 | **Ratified**: 2025-10-10 | **Last Amended**: 2025-10-10

.specify/scripts/bash/check-prerequisites.sh

@@ -0,0 +1,166 @@
+#!/usr/bin/env bash
+
+# Consolidated prerequisite checking script
+#
+# This script provides unified prerequisite checking for Spec-Driven Development workflow.
+# It replaces the functionality previously spread across multiple scripts.
+#
+# Usage: ./check-prerequisites.sh [OPTIONS]
+#
+# OPTIONS:
+#   --json              Output in JSON format
+#   --require-tasks     Require tasks.md to exist (for implementation phase)
+#   --include-tasks     Include tasks.md in AVAILABLE_DOCS list
+#   --paths-only        Only output path variables (no validation)
+#   --help, -h          Show help message
+#
+# OUTPUTS:
+#   JSON mode: {"FEATURE_DIR":"...", "AVAILABLE_DOCS":["..."]}
+#   Text mode: FEATURE_DIR:... \n AVAILABLE_DOCS: \n ✓/✗ file.md
+#   Paths only: REPO_ROOT: ... \n BRANCH: ... \n FEATURE_DIR: ... etc.
+
+set -e
+
+# Parse command line arguments
+JSON_MODE=false
+REQUIRE_TASKS=false
+INCLUDE_TASKS=false
+PATHS_ONLY=false
+
+for arg in "$@"; do
+    case "$arg" in
+        --json)
+            JSON_MODE=true
+            ;;
+        --require-tasks)
+            REQUIRE_TASKS=true
+            ;;
+        --include-tasks)
+            INCLUDE_TASKS=true
+            ;;
+        --paths-only)
+            PATHS_ONLY=true
+            ;;
+        --help|-h)
+            cat << 'EOF'
+Usage: check-prerequisites.sh [OPTIONS]
+
+Consolidated prerequisite checking for Spec-Driven Development workflow.
+
+OPTIONS:
+  --json              Output in JSON format
+  --require-tasks     Require tasks.md to exist (for implementation phase)
+  --include-tasks     Include tasks.md in AVAILABLE_DOCS list
+  --paths-only        Only output path variables (no prerequisite validation)
+  --help, -h          Show this help message
+
+EXAMPLES:
+  # Check task prerequisites (plan.md required)
+  ./check-prerequisites.sh --json
+  
+  # Check implementation prerequisites (plan.md + tasks.md required)
+  ./check-prerequisites.sh --json --require-tasks --include-tasks
+  
+  # Get feature paths only (no validation)
+  ./check-prerequisites.sh --paths-only
+  
+EOF
+            exit 0
+            ;;
+        *)
+            echo "ERROR: Unknown option '$arg'. Use --help for usage information." >&2
+            exit 1
+            ;;
+    esac
+done
+
+# Source common functions
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/common.sh"
+
+# Get feature paths and validate branch
+eval $(get_feature_paths)
+check_feature_branch "$CURRENT_BRANCH" "$HAS_GIT" || exit 1
+
+# If paths-only mode, output paths and exit (support JSON + paths-only combined)
+if $PATHS_ONLY; then
+    if $JSON_MODE; then
+        # Minimal JSON paths payload (no validation performed)
+        printf '{"REPO_ROOT":"%s","BRANCH":"%s","FEATURE_DIR":"%s","FEATURE_SPEC":"%s","IMPL_PLAN":"%s","TASKS":"%s"}\n' \
+            "$REPO_ROOT" "$CURRENT_BRANCH" "$FEATURE_DIR" "$FEATURE_SPEC" "$IMPL_PLAN" "$TASKS"
+    else
+        echo "REPO_ROOT: $REPO_ROOT"
+        echo "BRANCH: $CURRENT_BRANCH"
+        echo "FEATURE_DIR: $FEATURE_DIR"
+        echo "FEATURE_SPEC: $FEATURE_SPEC"
+        echo "IMPL_PLAN: $IMPL_PLAN"
+        echo "TASKS: $TASKS"
+    fi
+    exit 0
+fi
+
+# Validate required directories and files
+if [[ ! -d "$FEATURE_DIR" ]]; then
+    echo "ERROR: Feature directory not found: $FEATURE_DIR" >&2
+    echo "Run /speckit.specify first to create the feature structure." >&2
+    exit 1
+fi
+
+if [[ ! -f "$IMPL_PLAN" ]]; then
+    echo "ERROR: plan.md not found in $FEATURE_DIR" >&2
+    echo "Run /speckit.plan first to create the implementation plan." >&2
+    exit 1
+fi
+
+# Check for tasks.md if required
+if $REQUIRE_TASKS && [[ ! -f "$TASKS" ]]; then
+    echo "ERROR: tasks.md not found in $FEATURE_DIR" >&2
+    echo "Run /speckit.tasks first to create the task list." >&2
+    exit 1
+fi
+
+# Build list of available documents
+docs=()
+
+# Always check these optional docs
+[[ -f "$RESEARCH" ]] && docs+=("research.md")
+[[ -f "$DATA_MODEL" ]] && docs+=("data-model.md")
+
+# Check contracts directory (only if it exists and has files)
+if [[ -d "$CONTRACTS_DIR" ]] && [[ -n "$(ls -A "$CONTRACTS_DIR" 2>/dev/null)" ]]; then
+    docs+=("contracts/")
+fi
+
+[[ -f "$QUICKSTART" ]] && docs+=("quickstart.md")
+
+# Include tasks.md if requested and it exists
+if $INCLUDE_TASKS && [[ -f "$TASKS" ]]; then
+    docs+=("tasks.md")
+fi
+
+# Output results
+if $JSON_MODE; then
+    # Build JSON array of documents
+    if [[ ${#docs[@]} -eq 0 ]]; then
+        json_docs="[]"
+    else
+        json_docs=$(printf '"%s",' "${docs[@]}")
+        json_docs="[${json_docs%,}]"
+    fi
+    
+    printf '{"FEATURE_DIR":"%s","AVAILABLE_DOCS":%s}\n' "$FEATURE_DIR" "$json_docs"
+else
+    # Text output
+    echo "FEATURE_DIR:$FEATURE_DIR"
+    echo "AVAILABLE_DOCS:"
+    
+    # Show status of each potential document
+    check_file "$RESEARCH" "research.md"
+    check_file "$DATA_MODEL" "data-model.md"
+    check_dir "$CONTRACTS_DIR" "contracts/"
+    check_file "$QUICKSTART" "quickstart.md"
+    
+    if $INCLUDE_TASKS; then
+        check_file "$TASKS" "tasks.md"
+    fi
+fi