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GNN advanced validation and consistency checking. Use when performing deep validation of GNN models, checking cross-model consistency, verifying structural integrity, or running validation reports.
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。
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-validation |
| description | GNN advanced validation and consistency checking. Use when performing deep validation of GNN models, checking cross-model consistency, verifying structural integrity, or running validation reports. |
Performs advanced validation beyond basic type checking, including semantic validation, performance profiling, consistency checking, and comprehensive validation reporting.
# Run validation
python src/6_validation.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 6 --verbose
from validation import (
SemanticValidator, process_semantic_validation,
PerformanceProfiler, profile_performance,
ConsistencyChecker, check_consistency,
process_validation
)
# Run full validation (used by pipeline)
result = process_validation(target_dir, output_dir, verbose=True)
# Semantic validation
validator = SemanticValidator()
result = process_semantic_validation(parsed_model)
# Consistency checking
checker = ConsistencyChecker()
result = check_consistency(parsed_models)
# Performance profiling
profiler = PerformanceProfiler()
result = profile_performance(parsed_model)
SemanticValidator / process_semantic_validation — Active Inference constraint validationPerformanceProfiler / profile_performance — model performance analysisConsistencyChecker / check_consistency — cross-model coherence checksprocess_validation — main pipeline processing functionoutput/6_validation_output/This module registers tools with the GNN MCP server (see mcp.py):
check_schema_complianceget_validation_reportprocess_validationvalidate_gnn_file