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Backend Node.js Specialist IA — Expert en développement backend Node.js (Express, Fastify, NestJS, Prisma, real-time, WebSockets)

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Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with $helpers, working with dates using DateTime, troubleshooting Code node errors, or choosing between Code node modes.

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pen.dev CLI（`pencil` / `pen`コマンド）だけを使って.penファイル（pen.devで作成されたデザインファイル）を`execute`による決定論的な編集操作で修正・更新・新規作成するスキル。.penファイルの編集、ボタン追加、レイアウト変更、UIデザインの調整、pen.devデザインの更新、新しい.penデザインの作成などの依頼で必ず使用する。有料の`--prompt`（エージェントモード）は使わず、編集・新規作成はすべてインタラクティブモード（`pencil interactive`）の`execute`（`Insert` / `Update` / `Delete` など）で行い、最後に`save()`する。`Insert` / `Update` に渡すNodeデータは`references/pen-format.md`の.penフォーマット定義に従う。既存ファイルは`-i`と`-o`に同一パスを指定して上書き、新規作成は`-i`を省略して`-o`に新しいパスを指定する。編集・作成後は「**編集・作成したコンポーネントのNodeだけ**」を`execute`の`Export` / `TakeScreenshot`でPNG出力し、`.pen`と同階層の`snapshots/`ディレクトリに保存する。pen.dev MCPには依存せず`pencil`コマンドのみで完結する。.penファイルのgitコンフリクト解消・破損復旧は本スキルではなく`resolve-pencil-conflict`スキルの担当。

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Debug Node.js via --inspect + Chrome DevTools Protocol CLI.

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Manage Node-RED instances via Admin API or CLI. Automate flow deployment, install nodes, and troubleshoot issues. Use when user wants to "build automation", "connect devices", or "fix node-red".

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Set up an ESP32-S3/C6 sensing node for RuView by choosing firmware, checking and flashing binaries, and configuring WiFi and capture settings. Use boot logs to confirm CSI, measurements of the WiFi channel, is being captured.

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• Choosing firmware for an ESP32 board
• Flashing firmware after checksum verification
• Configuring WiFi, channels, and MAC filters

RuView의 WiFi 설정, 감지 임계값, 기기 내 처리와 다중 노드 협업을 조정합니다. 기기 설정 기록부터 서버 설정과 Cognitum Seed 연동까지 다룹니다.

• WiFi 채널과 송신원 지정
• 재실·낙상·생체 정보 감지 조정
• 여러 노드의 협업 동작 설정

Help design quorum management for distributed systems, where multiple nodes must agree on decisions. Covers adapting participation thresholds and voting weights to network conditions, performance, and failures, plus membership changes, validation, and rollback.

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• Review quorum requirements under changing network conditions
• Plan node additions, removals, and failure handling
• Design weighted voting based on node capabilities

Generate images, video, and music through ComfyUI in OpenMontage. Helps adapt community workflows, select the final output, identify missing models, and choose workflows suited to available GPU memory while recording settings for reproducibility.

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• Generating images, video, or music in OpenMontage
• Importing a community ComfyUI workflow
• Choosing workflows for limited GPU memory

Manage Godot projects from the command line: create scenes, inspect their structure, add nodes, run GDScript, and check syntax. Build exports using configured presets, with JSON output available for agents.

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• Create a Godot project and its first scene
• Inspect scene structure and add nodes
• Run GDScript tools and check syntax

Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.

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• Setting up kube-bench for the first time
• Selecting benchmark versions and scan targets
• Exporting JSON or JUnit results for CI

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

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• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Define custom entities and relationships in cognee, a memory system for AI applications. This skill guides model design, identity matching, searchable fields, and troubleshooting when entities are duplicated or relationships are missing.

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• Designing custom entities and relationships
• Preventing duplicate entities across imports
• Investigating missing search results or relationships

Embed Zoom meetings in an Electron desktop app using the Meeting SDK. The skill guides developers through initialization, token-based authentication, joining and starting meetings, settings controls, and integration with raw meeting data.

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• Embed Zoom meetings in an Electron app
• Design authentication and meeting entry flows
• Organize SDK services, controllers, and events

Investigate growing memory usage in Qwen Code CLI by comparing heap snapshots, records of objects in memory. Trace what keeps objects alive, identify likely causes, and repeat the workload to verify a fix.

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• Investigating steadily growing memory usage
• Tracing references that keep objects alive
• Finding growing arrays, caches, or event listeners

Scan API route definitions and generate test files for authentication, input validation, error responses, pagination, uploads, and rate limits. Supports Next.js App Router, Express, FastAPI, and Django REST, with Node and Python test outputs.

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• Creating test scaffolds for a new API
• Adding baseline tests to an existing API
• Checking API behavior against current route definitions

Find the right specialist guidance for building, editing, testing, or debugging n8n workflows through n8n-mcp. This entry skill explains available tools, connection requirements, credential handling, and checks to perform before activating a workflow.

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• Finding the right skill for an n8n task
• Checking settings and connections before activation
• Investigating a workflow that behaves unexpectedly

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

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• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Plan and configure GKE cluster networks, including private nodes, IP address ranges, DNS, and outbound connectivity. Use the guidance to set up VPC-native networking and Dataplane V2, which provides built-in network policy enforcement.

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• Designing a private GKE cluster network
• Planning IP ranges for Pods, Services, and nodes
• Configuring outbound access with a static source IP

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

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• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

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• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Diagnose iii invocation failures using error codes across Node, Python, Rust, and browser workers. Distinguish permission denials, timeouts, and handler failures, then choose appropriate exception handling and decide whether retrying is suitable.

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• Diagnosing failed function calls by error code
• Deciding whether a timed-out operation should be retried
• Handling permission denials without blind retries