303 件 ・ 関連度順
概要と使いどころ
Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory.
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Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.
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Knowledge base from MIL-HDBK-516C (Airworthiness Certification Criteria). Use for DoD air-system airworthiness planning: tailoring a certification basis, systems engineering criteria, and domain clusters for structures, flight technology, propulsion/installation, avionics/electrical/E3/diagnostics, computer systems/software, and crew systems (including applicable UAS control-station elements). Qualitative guidance handbook — not raw contractual requirements. Does not replace service airworthiness authority policy, civil FAA certification rules as authorities, or detailed JSSG/MIL performance specs.
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Move or sync the hub role of an agent-plus-vault setup between computers, where an always-on desktop is the hub and laptops are satellites. Checklist with counters verified before and after, and it never migrates silently. Triggers: "/migrate", "/migrate check", "/migrate pack", "/migrate bootstrap".
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Read this machine's cross-machine mailbox, the messages another computer's agent left here over a file-synced bus folder, and send messages back the same way. A session-start hook shows new mail automatically; this is the on-demand mid-session re-check. Triggers: "/inbox", "check inbox", "messages from the other machine".
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Drive the user's host machine directly through Cua, with no sandbox. Maps to Cua's Localhost API. Higher risk - the agent can click, type, and run shell commands on the real desktop. Use only when the user explicitly opts in and read the safety section first. Triggers - "control my computer", "click here", "use my real Chrome", "no sandbox", "내 컴퓨터 자동화".
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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.
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Neuromorphic Computing - Fraunhofer Institute for … — Neuromorphic computing is a field of technology aimed at designing computer architectures that m
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Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories.
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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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Breadboard circuit mockups via HTML5 Canvas. Use when creating circuit layouts, visualizing 6502/retro electronics components, drawing breadboard diagrams, or designing vintage computer schematics with discrete parts.
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Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps
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How to use windows-computer-use-mcp effectively — tool selection, focus rules, safety, and mission design.
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Turn one sentence into a task line in the right to-do list, with project, priority and optional due date. Use for "add a to-do", "remember to ...", "put this on my list", "quick task". Nothing leaves your computer.
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Split one big task into chunks of at most 25 minutes, each with a done-check, and place the demanding ones in your high-energy hours. Use for "split this task", "break it into chunks", "I can't start this", "focus plan". Nothing leaves your computer.
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Bring a new machine to a verified AOS installation: read-only doctor baseline, install after GO, MCP servers wired per harness with the configs backed up first, memB and OpenWiki checks, and a second doctor run for the delta. Use for "set up this machine", "onboard a new computer", "install AOS on this laptop".
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Turn your scattered to-do lists and notes into one prioritised plan for the week, with at most 3 must-do items per day. Use for "plan my week", "what should I do this week", "sort my to-dos", "weekly plan from my notes". Nothing leaves your computer unless you opt in to steps 4 or 8.
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Bring a machine to a complete, verified AOS installation: the skills pack, MCP servers, gate hooks, memB (engine, MCP, ambient hook, WebUI), OpenWiki (CLI, auth, harness integrations, refresh daemon) and Synapse. Run once on a new computer, and again whenever something in the ecosystem stopped working.
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Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison. Use when `.transition` or visual effects are not observable via MotionEyes logs, when you only have screenshots/video, when you need regression diffs across builds, or when you need to summarize on-screen changes without instrumentation. Do not use when MotionEyes traces are available and you need precise timing/values; use `motioneyes-animation-debug` instead.
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Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.
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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
日本語の概要は準備中です。原文の説明を表示しています。