Skill UI

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Provides production-ready Megatron-Core workflows for training 2B–462B parameter LLMs with tensor/pipeline/expert/context parallelism, maximizing GPU efficiency on H100 clusters.

Provides production-ready TypeScript and Python patterns for TwinMind AI memory and meeting intelligence REST APIs, covering authenticated clients, memory storage/retrieval, meeting context creation, batching, and rate-limit aware retries to standardize integrations.

Creates customized Cascade agent configurations for domain-specific tasks using Windsurf tools, covering system prompts, context sources, activation triggers, and validation workflows for specialized AI behavior.

Windsurf Debugging AI uses Cascade to analyze stack traces, error logs, and context so developers can identify root causes, apply fixes, and document prevention steps during troubleshooting.

Reasoning-Driven Image Briefs skill transforms agent prompts into logic-based Gemini 3 style instructions for muapi.ai, adding structured creative briefs (subject/action/context/composition/lighting), precise text cues, and physics-grounded constraints before invoking generation scripts.

Self-hosted open-source alternative to NotebookLM for organizing research, ingesting diverse sources, generating AI notes, running context-aware chats, performing vector search, producing multi-speaker podcasts, and automating workflows via a REST API.

Hierarchical Agent Memory (HAM) scopes Claude Code agent context by generating per-directory CLAUDE.md files, tracking token savings, and surfacing analytics via a dashboard so multi-folder projects avoid repeated full scans.

This tool is designed for defining and scaffolding production-grade, multi-agent workflows. It allows developers to move beyond single prompts by structuring complex tasks into explicit steps. Core capabilities include selecting workflow patterns (e.g., sequential, parallel, orchestrator), defining handoff contracts, implementing error recovery strategies, and enforcing context and cost discipline across long-running AI processes.

Helps product managers diagnose whether AI workflows rely on indiscriminate context stuffing or deliberate context engineering, then apply bounded domains, retrieval, and Research→Plan→Reset→Implement practices to improve reliability and output quality.

Designs and optimizes AI agent execution by defining action spaces, tools, observation formats, recovery contracts, and context-budget strategies to boost completion rates and reliability.

Converts RDF context into belief-desire-intention structures so agents can track perceptions, deliberate plans, and emit explainable RDF, supporting BDI frameworks or LLMs needing structured cognition.