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SkillsMP has collected 35 skills from ActiveInferenceInstitute/GeneralizedNotationNotation. Open a skill to review its source and details.
Showing 35 of 35 collected skills.
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 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.
GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs.
GNN audio generation and sonification. Use when creating audio representations of GNN models, generating sonification of state spaces, or working with SAPF and Pedalboard audio backends.
GNN file discovery, parsing, and multi-format serialization. Use when reading GNN model files, parsing StateSpaceBlock definitions, extracting connections, validating GNN syntax, or converting between GNN formats.
GNN interactive GUI for constructing and editing GNN models. Use when building visual model editors, launching the GNN GUI application, or working with the multi-panel GUI system (gui_1, gui_2, gui_3, oxdraw).
GNN Active Inference ontology processing and validation. Use when working with ActInfOntologyAnnotation sections, mapping GNN variables to ontology terms, validating semantic annotations, or exploring Active Inference concept hierarchies.
GNN research tools and experimental features. Use when running experimental analyses, prototyping new pipeline features, conducting research experiments on GNN models, or exploring novel Active Inference patterns.
GNN security validation and access control. Use when auditing security of generated code, validating input sanitization, checking dependency vulnerabilities, or enforcing security policies on pipeline outputs.
GNN structural type checking and resource estimation. Use when validating GNN dimensions, syntax, type consistency, or generated type-checker reports.
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.
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.
GNN code generation for simulation frameworks. Use when generating PyMDP, RxInfer.jl, ActiveInference.jl, JAX, DisCoPy, PyTorch, NumPyro, Stan, or bnlearn code from GNN model specifications.
Capabilities for API
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 simulation script execution with result capture. Use when running generated simulation scripts, managing execution environments, handling framework dependencies, or capturing simulation outputs and metrics.
oxdraw visual editor integration for GNN. Use when launching the oxdraw interactive visual editor, converting GNN files to Mermaid flowchart format, or compiling edited Mermaid files back into GNN markup.
GNN system integration and cross-module coordination. Use when coordinating data flow between pipeline steps, resolving cross-module dependencies, or configuring inter-step communication.
GNN AI-powered pipeline analysis and executive reports. Use when generating AI-driven executive summaries, performing intelligent pipeline health assessments, or creating comprehensive AI-enhanced analysis of GNN processing results.
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 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 machine learning integration and model training. Use when training ML models on GNN data, checking ML framework availability, or integrating GNN pipeline outputs with machine learning workflows.
GNN comprehensive analysis report generation. Use when creating summary reports from pipeline outputs, generating markdown or HTML reports, or producing executive summaries of model processing results.
GNN static HTML website generation from pipeline artifacts. Use when generating browsable documentation websites, creating HTML galleries of model visualizations, or publishing pipeline results as a static site.
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.
GNN model versioning and registry management. Use when registering parsed models, tracking model versions, querying model metadata (author, license, version), or managing the model catalog.
GNN environment setup and dependency management. Use when configuring the development environment, installing dependencies, managing virtual environments, or troubleshooting dependency issues for the GNN pipeline.
GNN advanced visualization and interactive plots. Use when creating D2 diagrams, dashboards, interactive network visualizations, timeline charts, heatmaps, or data extraction for custom visualizations.
Source-adjacent GNN implementation documentation. Use when working with static reference notes under `src/doc/` or checking that implementation-near documentation is intentionally non-runtime.
GNN multi-format export generation. Use when exporting GNN models to JSON, XML, GraphML, GEXF, Pickle, or other interchange formats for use in external tools and frameworks.
Core Graphical User Interface module for GNN. Use when querying GUI availability, checking GUI export paths, and managing the main interactive interface lifecycle.
GNN Language Server Protocol integration. Use when starting editor diagnostics, checking hover behavior, or validating LSP support for GNN files.
Capabilities for SAPF
GNN pipeline template and initialization system. Use when creating new pipeline steps, bootstrapping project structure, or understanding the thin orchestrator pattern for GNN module development.