ASISaga

Bidirectional design token translation between Style Dictionary JSON and _variables.scss for the Genesis Semantic Design System. Converts tokens to SCSS variables and parses existing SCSS back into structured token JSON.

Apply advanced glassmorphism, neon glows, quantum gradients, and consciousness animations using Genesis Ontological mixins. Implement futuristic visual effects while maintaining semantic purity. Use when enhancing visual aesthetics, creating immersive experiences, or implementing advanced UI effects from v2.0+ enhancements.

Migrate legacy CSS and SCSS to Genesis Ontological mixins. Convert raw CSS properties to semantic ontological patterns, ensure zero-CSS compliance in subdomain files, and maintain the three-tier architecture. Use when refactoring existing stylesheets to adopt the ontology system or auditing subdomain SCSS for semantic purity.

Validates SCSS unit compatibility to prevent Sass compilation errors from mixing incompatible units like vw and rem

Manage bidirectional synchronization between _design/ JSON blueprints and Jekyll _includes/_layouts code. _design/ is the single source of truth for both Jekyll and Figma. Forward-syncs blueprints to _data/design/ for Jekyll. Reverse-extracts structural changes from HTML back to blueprints. Keeps genesis-core.scss selectors in step with layout-variants. Enriches blueprints with figmaStyles for Blueprint2Code import.

Author and validate JSON blueprints in _design/ that represent the skeletal structural intent of UI components and page layouts, entirely decoupled from visual styling or framework-specific code.

Create semantic HTML5 templates with accessibility-first principles for Genesis Ontological Design System. Ensure proper landmark elements, meaningful class names, WCAG compliance, and BEM-style naming. Includes ready-to-use component patterns for navigation, forms, cards, modals, and more. Use when building Jekyll templates, includes, or auditing HTML structure for semantic correctness.

Implement Motion.dev (WAAPI) animations with Genesis token-driven spring physics, scroll reveals, and gesture support while respecting prefers-reduced-motion. Use when adding transitions, entrance animations, or gesture-driven motion to Genesis theme components.

Enrich CEO (Steve Jobs) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich CFO (Warren Buffett) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich CHRO (Peter Drucker) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich CMO (Seth Godin) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich COO (W. Edwards Deming) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich CSO (Sun Tzu) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich CTO (Alan Turing) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Enrich Founder (Paul Graham) boardroom agent JSON-LD state file with legend-derived context enrichment from the boardroom-agents spec

Use Genesis language to describe, analyze, and improve Genesis itself - meta-programming for self-improvement and validation

Run the Genesis code formatter to enforce consistent style in Genesis programs, ensuring 4-space indentation and proper formatting

Run the Genesis static analyzer to check programs for structural correctness, naming conventions, and alignment with Genesis philosophy

Write Genesis programs that generate, analyze, transform, and validate other Genesis programs - meta-level programming capabilities

Launch the interactive Genesis REPL for exploratory development, testing declarations, and debugging resonance scoring in real-time

Execute Genesis programs using the reference interpreter to validate program correctness and test language features

Enable Genesis to evolve itself through self-modification, creating an Ouroboros loop of continuous improvement while preserving core essence

Meta-agent that evolves the agent intelligence system through dogfooding, continuous learning, and specification-driven optimization for maximum context window efficiency

Manage documentation lifecycle, enforce standards, validate structure, detect redundancy, and ensure proper archival. Automate documentation quality checks with validation scripts. Use when creating, updating, or organizing documentation across the repository to maintain single source of truth and prevent documentation drift.

Bootstrap GitHub Copilot agent intelligence system in new repositories with complete setup

Manage specification-driven development workflow for .github/specs/. Create, validate, and update feature specifications, implementation plans, and task lists. Use when creating or updating specs for the repository using the Copilot agent intelligence system.

Expert knowledge of the Agent Operating System (AOS) architecture, components, and design patterns. Provides deep understanding of how AOS works as a complete operating system for AI agents.

Expert knowledge for testing asynchronous Python code in the Agent Operating System. AOS is heavily async-based, and proper async testing patterns are critical for reliable tests.

Master Pylint usage for maintaining high code quality in the Agent Operating System (AOS) repository. Includes static code analysis, error detection, PEP 8 enforcement, and code quality metrics.

Expert knowledge for performing major version refactoring in the Agent Operating System, including removing backward compatibility code, consolidating duplicate implementations, and updating all references to use the latest patterns.

Debug and troubleshoot production Azure infrastructure issues in the Agent Operating System. Covers systematic diagnostics, log analysis with KQL, resource health checks, and resolution of deployment failures, performance problems, and connectivity issues. USE FOR: debug Azure issues, troubleshoot deployment failures, analyze logs with KQL, fix connectivity issues, resolve resource provisioning errors, investigate health probe failures, check resource health, view application logs, find root cause of errors, diagnose AOS Function App issues, fix Service Bus connection failures, resolve Key Vault access problems. DO NOT USE FOR: deploying infrastructure (use deployment-error-fixer), creating new resources (use azure-prepare), setting up monitoring alerts (use azure-observability), cost analysis (use azure-cost-optimization).

Autonomously detect and fix logic errors in Python and Bicep deployment code for the AOS infrastructure. Handles syntax errors, validation failures, and common deployment issues to enable fully autonomous deployment workflows. USE FOR: fix Python syntax errors, fix Bicep BCP errors, resolve deployment logic failures, fix missing imports, fix parameter validation errors, auto-repair deployment code, pre-deployment validation, lint → validate → what-if → deploy pipeline. DO NOT USE FOR: environmental deployment failures (retry with backoff instead), infrastructure architecture changes (use azure-prepare), compliance scanning (use azure-compliance), cost analysis (use azure-cost-optimization).

Validate and enforce zero-CSS compliance in subdomain page-specific SCSS files. Ensure all styling uses only Genesis Ontological mixins with no raw CSS properties, unit values, color values, or imports. Use when auditing or creating subdomain page-specific SCSS in _sass/main.scss.

Identify semantic gaps in the Genesis Ontological Design System and create well-formed propositions for new variants. Use when subdomain development reveals missing semantic patterns that cannot be expressed with current ontological mixin combinations.

Expert knowledge for developing, deploying, and debugging Azure Functions in the Agent Operating System (AOS). Covers the serverless deployment model used by AOS for production workloads, including integration with Microsoft Foundry Agent Service (Azure AI Agents runtime).

Expert knowledge for developing, extending, testing, and deploying the aos-intelligence package — the ML/AI intelligence layer of the Agent Operating System. Covers ML pipelines, LoRAx multi-adapter serving, DPO training, self-learning systems, knowledge management, RAG engine, and integration with purpose-driven agents via the IMLService interface.

Expert knowledge for working with PurposeDrivenAgent — the abstract base class and fundamental building block of the Agent Operating System (AOS). Covers perpetual operation, LoRA adapter mapping, MCP context preservation, purpose alignment, goal tracking, and the GenericPurposeDrivenAgent concrete implementation. Enables efficient development, debugging, and testing of purpose-driven perpetual agent implementations.

Expert knowledge for working with CMOAgent — a dual-purpose perpetual agent that maps both Marketing and Leadership purposes to separate LoRA adapters. Covers execute_with_purpose(), get_adapter_for_purpose(), get_status(), the purpose-to-adapter mapping pattern, adapter switching internals, and integration with LeadershipAgent and PurposeDrivenAgent.