メニュー
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.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation --skill gnn-shared-utilitiesコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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 comprehensive test suite execution and management. Use when running tests, writing new test cases, checking coverage, debugging test failures, or validating pipeline correctness across all 25 GNN modules.
GNN graph and matrix visualization generation. Use when creating network graph plots, matrix heatmaps, state space diagrams, or other visual representations of GNN models.
SKILL.md を表示中
| 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 | config_loader, get_config_value, set_config_value, 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
config_loader,
get_config_value,
set_config_value,
validate_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
from pipeline.config import get_pipeline_config
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