Apply systematic problem-solving techniques for complexity spirals (simplification cascades), innovation blocks (collision-zone thinking), recurring patterns (meta-pattern recognition), assumption constraints (inversion exercise), scale uncertainty (scale game), and dispatch when stuck. Techniques derived from Microsoft Amplifier project patterns adapted for immediate application.
Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.
Use when the user asks to analyze package upgrades, check for outdated dependencies, plan npm/NuGet updates, or assess breaking changes in package updates. Triggers on keywords like "upgrade packages", "outdated", "npm update", "breaking changes", "dependency upgrade", "package update", "version upgrade".
Multi-perspective AI consultation. Invoke with /tzurot-council-mcp for major refactors (>500 lines), structured debugging after failed attempts, or when a technical decision has multiple viable approaches.
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
Guide users through writing their first Buck2 rule to learn fundamental concepts including rules, actions, targets, configurations, analysis, and select(). Use this skill when users want to learn Buck2 basics hands-on or need help understanding rule writing.
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Build, test, and serve the documentation site using Zensical (recommended) or MkDocs.
Submit, monitor, analyze, and evaluate LeRobot imitation learning training jobs on OSMO with Azure ML MLflow integration and inference evaluation - Brought to you by microsoft/physical-ai-toolchain
Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.
Implement production-ready features across database, backend, and frontend layers with incremental phased approach
Use when the user asks to generate comprehensive feature documentation with verified test cases, create feature README with code evidence, or document a complete feature with test verification. Triggers on keywords like "feature documentation", "document feature", "comprehensive docs", "feature README", "test verification", "verified documentation".
Start and interact with the Dataset Analysis Tool (dataviewer) for browsing, annotating, and exporting robotic training episodes
Comprehensive code refactoring operations for improving code quality and maintainability
Use when the user asks to debug, diagnose, fix a bug, troubleshoot errors, investigate issues, or pastes error messages/stack traces. Triggers on keywords like "bug", "error", "fix", "not working", "broken", "debug", "stack trace", "exception", "crash", "issue".
Use when the user asks to implement a new feature, enhancement, add functionality, build something new, or create new capabilities. Triggers on keywords like "implement", "add feature", "build", "create new", "develop", "enhancement".
Principles for designing context-efficient AI agents and tools. Use when designing LLM tools, agents, MCP servers, or multi-agent systems.
Apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification. Use for complex problem decomposition, adaptive planning, analysis needing course correction, problems with unclear scope, multi-step solutions, and hypothesis-driven work.
Use when the user provides an implementation plan file and asks to analyze it, assess impact, update specifications, or verify planned changes. Triggers on keywords like "analyze plan", "implementation plan", "assess impact", "update spec from plan", "verify plan".
UniTask library expert specializing in allocation-free async/await patterns, coroutine migration, and Unity-optimized asynchronous programming. Masters UniTask performance optimizations, cancellation handling, and memory-efficient async operations. Use PROACTIVELY for UniTask implementation, async optimization, or coroutine replacement.