Phase 1 - Session Bootstrap: - Add .claude/ATOMIZER_CONTEXT.md as single entry point for new sessions - Add study state detection and task routing Phase 2 - Code Deduplication: - Add optimization_engine/base_runner.py (ConfigDrivenRunner) - Add optimization_engine/generic_surrogate.py (ConfigDrivenSurrogate) - Add optimization_engine/study_state.py for study detection - Add optimization_engine/templates/ with registry and templates - Studies now require ~50 lines instead of ~300 Phase 3 - Skill Consolidation: - Add YAML frontmatter metadata to all skills (versioning, dependencies) - Consolidate create-study.md into core/study-creation-core.md - Update 00_BOOTSTRAP.md, 01_CHEATSHEET.md, 02_CONTEXT_LOADER.md Phase 4 - Self-Expanding Knowledge: - Add optimization_engine/auto_doc.py for auto-generating documentation - Generate docs/generated/EXTRACTORS.md (27 extractors documented) - Generate docs/generated/TEMPLATES.md (6 templates) - Generate docs/generated/EXTRACTOR_CHEATSHEET.md Phase 5 - Subagent Implementation: - Add .claude/commands/study-builder.md (create studies) - Add .claude/commands/nx-expert.md (NX Open API) - Add .claude/commands/protocol-auditor.md (config validation) - Add .claude/commands/results-analyzer.md (results analysis) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
184 lines
5.2 KiB
Python
184 lines
5.2 KiB
Python
"""
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Template Registry for Atomizer
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Provides study templates for common optimization scenarios.
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Used by Claude to quickly create new studies via wizard-driven workflow.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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REGISTRY_PATH = Path(__file__).parent / "registry.json"
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def load_registry() -> Dict[str, Any]:
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"""Load the template registry."""
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with open(REGISTRY_PATH, 'r') as f:
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return json.load(f)
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def list_templates() -> List[Dict[str, Any]]:
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"""List all available templates with summary info."""
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registry = load_registry()
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templates = []
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for t in registry["templates"]:
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templates.append({
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"id": t["id"],
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"name": t["name"],
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"description": t["description"],
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"category": t["category"],
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"n_objectives": len(t["objectives"]),
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"turbo_suitable": t.get("turbo_suitable", False),
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"example_study": t.get("example_study")
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})
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return templates
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def get_template(template_id: str) -> Optional[Dict[str, Any]]:
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"""Get a specific template by ID."""
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registry = load_registry()
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for t in registry["templates"]:
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if t["id"] == template_id:
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return t
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return None
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def get_templates_by_category(category: str) -> List[Dict[str, Any]]:
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"""Get all templates in a category."""
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registry = load_registry()
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return [t for t in registry["templates"] if t["category"] == category]
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def list_categories() -> Dict[str, Dict[str, str]]:
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"""List all template categories."""
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registry = load_registry()
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return registry.get("categories", {})
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def get_extractor_info(extractor_id: str) -> Optional[Dict[str, Any]]:
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"""Get information about a specific extractor."""
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registry = load_registry()
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return registry.get("extractors", {}).get(extractor_id)
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def suggest_template(
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n_objectives: int = 1,
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physics_type: str = "structural",
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element_types: Optional[List[str]] = None
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) -> Optional[Dict[str, Any]]:
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"""
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Suggest a template based on problem characteristics.
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Args:
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n_objectives: Number of objectives (1 = single, 2+ = multi)
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physics_type: Type of physics (structural, dynamics, optics, multiphysics)
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element_types: List of element types in the mesh
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Returns:
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Best matching template or None
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"""
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registry = load_registry()
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candidates = []
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for t in registry["templates"]:
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score = 0
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# Match number of objectives
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t_obj = len(t["objectives"])
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if n_objectives == 1 and t_obj == 1:
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score += 10
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elif n_objectives > 1 and t_obj > 1:
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score += 10
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# Match category
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if t["category"] == physics_type:
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score += 20
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# Match element types
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if element_types:
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t_elements = set(t.get("element_types", []))
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user_elements = set(element_types)
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if t_elements & user_elements:
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score += 15
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if "CQUAD4" in user_elements and "shell" in t["id"].lower():
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score += 10
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if score > 0:
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candidates.append((score, t))
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if not candidates:
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return None
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# Sort by score descending
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candidates.sort(key=lambda x: x[0], reverse=True)
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return candidates[0][1]
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def format_template_summary(template: Dict[str, Any]) -> str:
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"""Format a template as a human-readable summary."""
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lines = [
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f"**{template['name']}**",
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f"_{template['description']}_",
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"",
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f"**Category**: {template['category']}",
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f"**Solver**: {template.get('solver', 'SOL 101')}",
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"",
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"**Objectives**:"
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]
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for obj in template["objectives"]:
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lines.append(f" - {obj['name']} ({obj['direction']}) → Extractor {obj['extractor']}")
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lines.append("")
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lines.append("**Recommended Trials**:")
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trials = template.get("recommended_trials", {})
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for phase, count in trials.items():
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lines.append(f" - {phase}: {count}")
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if template.get("turbo_suitable"):
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lines.append("")
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lines.append("✅ **Turbo Mode**: Suitable for neural acceleration")
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if template.get("notes"):
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lines.append("")
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lines.append(f"⚠️ **Note**: {template['notes']}")
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if template.get("example_study"):
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lines.append("")
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lines.append(f"📁 **Example**: studies/{template['example_study']}/")
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return "\n".join(lines)
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def get_wizard_questions(template_id: str) -> List[Dict[str, Any]]:
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"""Get wizard questions for a template."""
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template = get_template(template_id)
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if not template:
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return []
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return template.get("wizard_questions", [])
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if __name__ == "__main__":
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# Demo: list all templates
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print("=== Atomizer Template Registry ===\n")
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for category_id, category in list_categories().items():
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print(f"{category['icon']} {category['name']}")
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print(f" {category['description']}\n")
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print("\n=== Available Templates ===\n")
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for t in list_templates():
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status = "🚀" if t["turbo_suitable"] else "📊"
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print(f"{status} {t['name']} ({t['id']})")
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print(f" {t['description']}")
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print(f" Objectives: {t['n_objectives']} | Example: {t['example_study'] or 'N/A'}")
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print()
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