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Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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Load when creating, scaffolding, configuring, debugging, testing, containerizing, or publishing a C#/.NET Model Context Protocol (MCP) server using the official Microsoft ModelContextProtocol SDK; not for non-.NET MCP servers, general ASP.NET Core APIs without MCP, or consuming external MCP tools as a client.

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Operational guide for the NV oOS Dictation desktop companion app (Tauri v2 + Rust, examples/nvoos-dictate) — build/run on Windows/MSVC, the local-first engine track (parakeet/whisper/fastconformer/moonshine/silero-vad), the embedded remote-STT fallback, the Stage-2 polish provider track (disabled/openai_compat/nvoos plus local LM Studio and Ollama), the file/media decode seam (Symphonia 0.5), the live camera + local object-detection/tracking track (Proposals 073 + 075) with an opt-in VLM label-polish layer (Proposal 076), the CDP browser-control agent (feature agent) + FastConformer engine + NV oOS multi-SPA hub, model registry + verification harnesses, the plugins.updater boot-crash config rule, and the full test/clippy gate matrix. Use when building, extending, or debugging the dictation app, adding an engine/model, a polish provider, the browser agent, file/video transcription, camera/vision detection, or VLM relabeling.

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Manage AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, and cross-assistant communication. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, or debugging assistant behavior.

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Manage AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, cross-assistant communication, and detecting from a coding-agent session (Zed) which NV oOS assistants / MCP servers are enabled. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, debugging assistant behavior, or answering "is agent X available" / "what tools does this MCP have".

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Use when working with Dbt Cloud — dbt Cloud management — monitor projects, environments, job runs, model status, and data freshness. Use when reviewing job run health, debugging failed models, inspecting run artifacts, or auditing project configuration.

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This skill should be used when the user wants to "test a claim against data", "validate an empirical hypothesis", "refine a model/theory with evidence", "characterize a pattern and verify it", "演繹と帰納で検証", "仮説をデータで検証して更新", "実例と反例で確かめる", "弁証法ループ", "主張を現物で裏取り", "傾向分析を検証して精緻化", or mentions iteratively updating a hypothesis by deriving predictions and testing them against a real corpus. NOTE: Use this for VALIDATING/REFINING an empirical claim or model against real data through a derive→test→update loop. Use strong-inference for debugging an UNKNOWN cause, and devils-advocate for adversarially stress-testing a DESIGN proposal.

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Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

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帮助设计和实现 TypeScript 后端中的 Prisma 数据库代码，通过具体示例检查查询、批量更新、迁移和连接管理中的常见问题。

• 设计 Prisma 模型及关联
• 检查查询与分页逻辑
• 排查事务和批量更新问题

使用mcp-use设计和实现MCP服务器，让AI调用外部操作与数据。指南涵盖认证、交互界面、错误处理及部署，并引导查阅各功能的配套资料。

• 新建MCP服务器
• 添加操作、数据及可复用提示词
• 构建浏览与选择数据的React界面

Implement and debug astronomical data analysis with Astropy in Python. Work with physical units, celestial coordinates, observation files, tables, precise time conversions, and cosmological models to prepare data and calculate astronomical quantities.

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• Convert celestial coordinates and physical units
• Read and edit FITS observation files
• Match objects across astronomical catalogs

Build and debug interactive web 3D scenes with Three.js, React Three Fiber, or Spline. Plan product configurators and portfolios, prepare models, connect scroll interactions, and review loading feedback, fallback content, accessibility, and performance on target devices.

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• Adding interactive 3D product previews and variant controls
• Creating a portfolio scene linked to scrolling
• Investigating slow loading or rendering in a 3D scene

Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.

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• Validating user input and API parameters
• Designing exception types and clear error messages
• Continuing batch jobs after individual failures

Build a lasting Markdown knowledge base for project decisions, architecture, and session discoveries. Search pages by keywords and tags, use results to answer with citations, and check for outdated content, broken links, and structural problems.

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• Preserving project decisions across sessions
• Finding past debugging discoveries
• Checking knowledge pages for broken links and stale content

Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.

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• Building a chatbot that uses conversation history
• Creating a question-answering system for documents
• Developing an agent that calls external tools

Get guidance on distributed training with PyTorch FSDP, which splits model parameters across devices. The skill covers implementation, mixed precision, moving data to CPU memory, communication setup, and debugging distributed training code.

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• Implement distributed training with FSDP
• Review mixed precision and CPU offloading settings
• Choose a distributed communication backend

Create, edit, and validate URDF files describing robot structure. Check links, joints, coordinate frames, units, mesh placement, and mass properties, then use CAD Viewer to inspect the model and review each movable joint.

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• Create a robot structure description
• Debug joint positions and motion axes
• Check mesh scale and placement

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

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• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

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• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs

Build MCP servers and clients that connect AI systems to external tools and data. The skill defines validated inputs, implements tool and resource handlers, configures communication methods, and checks protocol behavior with the MCP Inspector.

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• Expose external tools and data to AI systems through MCP
• Start an MCP project in TypeScript or Python
• Validate inputs and investigate protocol errors

Compare major AI coding tools and consult practical guidance on prompts, project instructions, debugging, workflows, and costs. Use the reference to choose suitable tools and identify why an AI-assisted coding task is stuck.

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• Comparing AI coding tools for a task
• Improving prompts and project instructions
• Troubleshooting stalled AI coding tasks

Create, edit, and troubleshoot Typst documents, a system for producing formatted documents from text files. Uses bundled documentation to check syntax and styling, with formatting checks and compilation when the required tools are available.

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• Creating a new Typst document
• Adjusting page layout, tables, and heading styles
• Fixing syntax and compilation errors

Implement, debug, or review Nexent frontend pages and React components using existing architecture and UI guidance. Cover API connections, shared data and types, styling, and localization, with checks for loading states, errors, and affected behavior.

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• Implementing or fixing Nexent screens
• Reviewing React components and hooks
• Checking API connections and shared types

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

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• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings