@effect/ai integration patterns for categorical AI composition, typed error handling, and production prompt pipelines. Use when building AI applications with Effect-TS, composing LLM calls with typed errors, creating tool-augmented AI systems, implementing structured output generation, or integrating multiple AI providers (OpenAI, Anthropic) with categorical composition patterns.
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool integrations, or streaming applications with the openai-agents package.
Comprehensive AI writing detection patterns and methodology. Provides vocabulary lists, structural patterns, model-specific fingerprints, and false positive prevention guidance. Use when analyzing text for AI authorship or understanding detection patterns.
LMQL constraint-guided generation DSL for type-safe prompting with grammar constraints and logical conditions. Use when building structured LLM outputs with guaranteed format compliance, implementing constrained decoding with logical operators, creating type-safe prompt templates, or combining neural generation with symbolic constraints for reliable AI outputs.
Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints
Validate datasets for Unsloth fine-tuning. Use when the user wants to check a dataset, analyze tokens, calculate Chinchilla optimality, or prepare data for training.
AshAi extension guidelines for integrating AI capabilities with Ash Framework. Use when implementing vectorization/embeddings, exposing Ash actions as LLM tools, creating prompt-backed actions, or setting up MCP servers. Covers semantic search, LangChain integration, and structured outputs.
OpenProse is a programming language for AI sessions. An AI session is a Turing-complete computer; OpenProse structures English into unambiguous control flow. OpenHands-Tab integration: - This repo vendors the OpenProse VM docs as your Skill. - When you see a `.prose` program, use `prose.md` for execution semantics and `docs.md` for syntax/validation. Activate when: running `.prose` files, mentioning OpenProse, or when a task is best expressed as an orchestrated multi-agent workflow.
This skill should be used when the user asks to "/gobby-agents", "spawn agent", "start agent", "list agents". Manage subagent spawning - start, cancel, list, and check results of autonomous agents.
Generate Unsloth training notebooks and scripts. Use when the user wants to create a training notebook, configure fine-tuning parameters, or set up SFT/DPO/GRPO training.
Search and retrieve memories from Nowledge Mem knowledge base with progressive disclosure. This skill should be used when the user asks to search memories, recall past knowledge, find saved information, look up conversation history, expand thread details, or mentions keywords like "记忆", "知识库", "之前说过", "我保存的", "历史对话".
Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.
Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI. Detects installed AI SDKs and configures appropriate integrations.
Analyze repository structure and generate standardized AGENTS.md files that serve as contributor guides for AI agents. Measures LOC to determine character limits and produces 5-section documents covering overview, folder structure, patterns, conventions, and working agreements.
実在する専門家の思想をエージェントに移植し、その専門性と思考パターンを再現するスキル。 Anchors: • 『心の社会』（Marvin Minsky） / 適用: 複雑系とエージェントシステム / 目的: 小規模エージェントの集合知実現 • 『ファスト&スロー』（Daniel Kahneman） / 適用: 意思決定と思考パターン / 目的: 専門家の直感と論理の統合 Trigger: Use when designing or improving agent personas, modeling expert thinking patterns, upgrading existing personas, or building multi-persona systems. persona design, expert modeling, thinking patterns, agent architecture, multi-agent systems
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
LLM4S Scala functional LLM interfaces with Effect system integration. Use when building LLM applications in Scala with ZIO or Cats Effect, implementing type-safe AI pipelines with functional error handling, creating composable prompt systems in Scala, or leveraging Scala's type system for robust AI applications.
Build Python TUI applications using the Textual framework. Use this skill when the user asks to create terminal user interfaces, CLI dashboards, TUI apps, or interactive terminal applications in Python. Generates production-grade Textual code with proper structure, styling, and event handling.
Creates new AI agent skills following the Agent Skills spec. Trigger: When user asks to create a new skill, add agent instructions, or document patterns for AI.
