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GNN shared utility functions and helper modules. Use when working with common pipeline utilities, logging helpers, file I/O wrappers, path management, or pipeline template infrastructure.
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SOC 職業分類に基づく
Generalized Notation Notation (GNN) processing pipeline for Active Inference generative models. Use when working with GNN files, running the 25-step pipeline, parsing model specifications, generating simulations, or producing visualizations and reports from GNN model definitions.
GNN command-line interface dispatch and health checks. Use when invoking the `gnn` CLI, validating subcommand availability, or checking command-line access to pipeline modules.
GNN LLM-enhanced analysis and model interpretation. Use when generating natural language descriptions of GNN models, getting AI-assisted model explanations, or performing LLM-powered analysis of Active Inference specifications.
GNN Language Server Protocol integration. Use when starting editor diagnostics, checking hover behavior, or validating LSP support for GNN files.
GNN Model Context Protocol processing and tool registration. Use when registering GNN operations as MCP tools, building MCP server configurations, or integrating GNN capabilities with LLM tool-use workflows.
GNN pipeline orchestration and configuration management. Use when configuring pipeline execution, setting step inclusion/exclusion, managing pipeline state, or customizing the 25-step execution flow.
| name | gnn-shared-utilities |
| description | GNN shared utility functions and helper modules. Use when working with common pipeline utilities, logging helpers, file I/O wrappers, path management, or pipeline template infrastructure. |
Provides shared utility functions, helper modules, and common infrastructure used across all 25 pipeline steps. Includes logging, error handling, configuration, dependency validation, and the standardized pipeline template.
# Utils is a shared library — not run directly but imported by all pipeline steps.
# Validate pipeline dependencies
python -c "from utils import validate_pipeline_dependencies; validate_pipeline_dependencies()"
# Check optional dependency status
python -c "from utils import get_dependency_status; print(get_dependency_status())"
# Run pipeline health check
python -c "from utils import generate_pipeline_health_report; print(generate_pipeline_health_report())"
| Module | Key Exports | Purpose |
|---|---|---|
pipeline_template | log_step_start, log_step_success, log_step_error, log_step_warning | Visual step logging |
logging | PipelineLogger, setup_step_logging, StructuredLogger | Structured logging |
error_handling | ErrorRecoveryManager, PipelineErrorHandler, generate_correlation_id | Error handling & recovery |
configuration | ConfigurationManager, get_config, set_config, validate_config | Pipeline configuration |
dependency | DependencyValidator, DependencyAuditor, validate_pipeline_dependencies | Dependency management |
performance | PerformanceTracker, track_operation_standalone | Performance monitoring |
from utils import (
# Logging
PipelineLogger, setup_step_logging, setup_main_logging,
log_step_start, log_step_success, log_step_error, log_step_warning,
StructuredLogger, get_pipeline_logger,
# Error handling
ErrorRecoveryManager, PipelineErrorHandler, generate_correlation_id,
format_error_message, get_recovery_manager,
# Configuration
ConfigurationManager, get_config, set_config, validate_config,
get_pipeline_config, load_config, save_config,
# Dependencies
DependencyValidator, validate_pipeline_dependencies,
check_optional_dependencies, get_dependency_status,
# Performance
PerformanceTracker, performance_tracker, get_performance_summary,
# Pipeline utilities
parse_arguments, validate_and_convert_paths,
get_output_dir_for_script, validate_output_directory,
BaseProcessor, ProcessingResult, create_processor
)
# Setup logging for a pipeline step
logger = setup_step_logging("step_name", verbose=True)
log_step_start(logger, "Processing started")
log_step_success(logger, "Processing completed")
# Configuration management
config = get_pipeline_config()
validate_config(config)
# Dependency validation
validate_pipeline_dependencies()
# Performance tracking
with performance_tracker.track("operation_name"):
do_work()
This module registers tools with the GNN MCP server (see mcp.py):
get_environment_infoget_file_infoget_logging_infoget_system_infovalidate_dependencies