「machine」の検索結果

Distributed consensus algorithms and logical time for cloud and multi-node systems. Covers Lamport clocks, vector clocks, FLP impossibility, Paxos (basic, multi, fast), Raft, Viewstamped Replication, Byzantine fault tolerance basics, quorum reads/writes (N/R/W), leader election, and TLA+ specification style. Use when designing replicated state machines, picking a consensus protocol, reasoning about split-brain and quorum loss, or writing formal specs for distributed coordination.

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Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine behavior.

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Generate and validate Mermaid diagrams (component, sequence, ER, deployment, state machine) for features. Use when documenting architecture.

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Advanced Zephyr RTOS kernel services. Covers inter-thread communication using Zbus (pub/sub), behavioral management with the State Machine Framework (SMF), background processing via work queues, and persistent configuration with the Settings subsystem. Trigger when building modular application architectures, complex state-driven logic, or requiring persistent data storage.

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Specialized hardware interfaces and system reliability for Zephyr RTOS. Covers LVGL GUI development, Audio I2S/Codecs, Watchdog timers, and Fault Injection. Trigger when building human-machine interfaces (HMI), audio devices, or high-reliability mission-critical systems.

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Machine learning environment for automated theorem proving with Coq.

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Lisp machine self-modification patterns via MCP Tasks and Narya bridge types

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Berry & Boudol's CHAM: computation as chemical reactions on multisets.

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Complement to alice-emacs-mods. Covers what alice does not — ghostel mastery, multi-daemon orchestration, cross-session coordination, debug/recovery harness, REPL-as-dynamical-citizen, QRTP distribution, strobe internals, and the hard-won lessons from spawning and debugging live REPLs under the server daemon. Read alongside alice-emacs-mods; together they tile the total Emacs-mod space on this machine.

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

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Build, publish, and verify a Deepline Play as a durable state machine over Customer DB tables, including a small paid pilot. Invoke this skill explicitly when the user asks to build an engine.

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Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation analysis, statistical methods (ADF/GARCH), strategy optimization, execution modeling, hedging, and ML-based prediction (sklearn). Also provides CLI access to run indicator scripts against K-line data. Triggers: "量化", "因子", "配对交易", "协整", "波动率策略", "季节性", "多因子", "IC", "机器学习", "对冲", "量化策略", "協整", "波動率策略", "季節性", "多因子", "對沖", "quant", "pairs trading", "cointegration", "volatility strategy", "seasonality", "multi-factor", "factor model", "IC IR", "machine learning", "hedging", "walk-forward", "配對交易", "機器學習", "因子選股"

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Extract visual identity from screenshots (primary) or website URL (fallback) and compile into a DESIGN.md-format brand kit — YAML token frontmatter (colors, typography, rounded, spacing, components) + 8 ordered prose sections. The YAML tokens are machine-authoritative; downstream visual skills consume them directly. Writes to marketing/brand/brand-kit.md. Triggers - "brand kit", "brand identity", "visual identity", "design tokens", "extract brand", "DESIGN.md"

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Measure whether a change made the tree simpler, on numbers rather than impressions — line counts, branching, coupling, interface width, rule count, standing exemptions and model-visible prose — across one diff or a whole stack of pull requests. Use before claiming a pass reduced complexity, when deciding which pull request in the stack added the machinery, and to read a function's branch count, which no v2 gate reports.

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Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

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Self-test the LLM's ability to execute ASE's core interpreter machinery (control flow, XML placeholders, regex matching, arithmetic) and report an overall compatibility rating. Use when the user wants to check how well the current model/harness is compatible with ASE, or asks to "test ASE compatibility" or "check ASE compatibility".

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Give your OpenClaw agent a homelab. Use when managing Docker containers, installing marketplace apps, or any Docker-related tasks on behalf of the user. Scope-based API key permissions — agent can only do what the key allows. On Linux, Docker requires sudo — without root access, WAGMIOS is the only safe interface for agent homelab control. Supports multi-machine management — one agent can manage multiple WAGMIOS instances across different hosts, each with its own scoped key. Requires X-API-Key header on every request (user provides at runtime). Includes Docker installation check and startup validation.

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Turn a brand's positioning into a 30-day, multi-platform content batch — 3-7 pillars, subtopic matrix mapped to funnel stage and persona, pillar pieces plus derivative fan-out, proof elements in at least half the slots, all registered in the editorial calendar store and quality-gated. Use when "content batch", "batch my content", "30 days of content", "month of content", "content batching machine", "content pillars", "fill the content calendar", "plan a month of posts", or any request to produce a full month of multi-platform content in one run.

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Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.

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Atuin replaces your existing shell history with a SQLite database that records additional context like exit codes, session IDs, working directories, and command durations. It provides encrypted cross-machine sync and a full-screen fuzzy search UI bound to Ctrl-R.

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Uses Lynis to run an on-host security audit and turn the findings into a prioritized hardening checklist for an agent or operator. Invoke it when a machine is about to become internet-facing, after base image changes, or whenever you need a quick read on hardening drift instead of a generic vulnerability scan.

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Use weekly to find and ship one new automation. Walks the 3Ms interview — Mindset (find the candidate) → Method (scope one) → Machine (build it). Trigger on "let's level up", "what should I automate next", "find me leverage this week", or as a Friday ritual. One run = one shipped artifact.

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Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.

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。