「machine」の検索結果

Use when a task needs the judgment of a Computer Numerically Controlled (CNC) Tool Programmer — calculating stepover from a scallop-height/surface-finish spec instead of picking one by feel, deciding whether a toolpath needs a dry run before cutting material on an unfamiliar post-processor/machine pairing, verifying datum consistency across multiple part setups, or diagnosing why cycle time jumped after a "minor" parameter change.

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。

Use when a task needs the judgment of an Agricultural Equipment Operator — calibrating planter seed population and singulation, diagnosing combine header or separator loss, sequencing planting or harvest across fields under a weather-constrained window, sizing machinery capacity in acres/hour against a season's acreage, or evaluating PTO/grain-bin entrapment safety on a job site.

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。

Download ENCODE genomics files (BED, FASTQ, BAM, bigWig, etc.) to the user's machine. Use when the user wants to download data files from ENCODE experiments.

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。

Creates Mermaid and ASCII diagrams for flowcharts, architecture, ERDs, state machines, mindmaps, and more. Use when user mentions diagram, flowchart, mermaid, ASCII diagram, text diagram, terminal diagram, visualize, C4, mindmap, architecture diagram, sequence diagram, ERD, or needs visual documentation.

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。

Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness 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。

Install and configure Tailscale across platforms. Detects OS, distro, and environment (including WSL2 and containers). Verifies existing installations, performs platform-appropriate install, and guides initial connection. Use when setting up Tailscale on a new machine, onboarding a server to a tailnet, or verifying an existing install.

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。

Generate KiCad schematics from a Circuit Weaver design.yaml or a project's own checked-in generator. Use for large, repetitive, or machine-synchronized designs. Prefer the Circuit Weaver CLI unless the project actually contains and documents its own generation scripts.

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。

Set up Muse's built-in Tailscale connector, join a tailnet or Headscale network, check status, and reach private machines through the TCP tunnel proxy. Read for Tailscale, VPN, MagicDNS, network egress, exit-node, or browser routing questions and supported limits.

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。

Analyze game AI systems including behavior trees, finite state machines, GOAP planning, utility AI scoring, A-star and NavMesh pathfinding, steering and flocking behaviors, perception and awareness models, dynamic difficulty adjustment, NPC dialogue trees and scheduling, boss AI patterns.

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。

Fetch the latest AI/ML news, trending open-source projects, social media discussions, and KOL (Key Opinion Leader) perspectives from 370+ curated sources. Use when the user asks about recent AI news, AI trends, what's happening in machine learning, open-source AI projects, AI Twitter/Reddit discussions, or wants a daily AI briefing.

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。

Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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。

Document a machine learning model in a structured, transparent format for stakeholders, reviewers, and future maintainers. Use when the user says "document this model", "write a model card", "model documentation", "how should I document my ML model", "bias and fairness report", "model transparency", "what does this model do", "model handoff", "production model documentation", or needs to communicate what a model does, how it was built, where it works, and where it fails.

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。

Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and evidence-backed explanations. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".

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。

Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT trigger for heavy production needs — use video-podcast-maker for those.

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。

Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.

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。

Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.

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。

Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

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。

Create a machine-readable phased JSON plan bundle from a spec or feature description. Produces a compact master manifest plus per-phase step files for resumable ck:cook execution.

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。

Create a machine-readable phased JSON plan bundle from a spec or feature description. Produces a compact master manifest plus per-phase step files for resumable ck:cook execution.

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。

Use when authoring, reviewing, or maintaining Salesforce DevOps operational documents — runbooks, environment matrices, deployment guides, and post-deploy validation checklists. Triggers: 'runbook', 'environment matrix', 'deployment guide', 'pre-deploy checklist', 'post-deploy validation', 'how do I document a deployment', 'rollback procedure template'. NOT for what actually belongs on the pre-deploy gate — use devops/pre-deployment-checklist. NOT for release calendar, versioning, or go/no-go governance — use devops/release-management. Also covers: the devops-process YAML artefact (environment ladder, change-request state machine, deploy contract, deploy order, RACI), the deployOptions the runbook must record (checkOnly, testLevel, rollbackOnError, ignoreWarnings, purgeOnDelete, singlePackage, runTests), destructiveChangesPre/Post ordering, and the change-request emergency path.

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。

How to use the coding agents installed on this machine (claude, codex, droid, grok, vktr, hermes, opencode, copilot, pi) as plain sub-agents in orchestrated workflows: one headless invocation, one prompt in, one result out. Deliberately ignores each CLI's own orchestration features (droid --mission, codex multi_agent, hermes delegation/moa/kanban, grok --agents, opencode personas). The orchestrator is whatever session is running the skill; the CLIs are interchangeable workers. Use when the user asks "which agent should run this", "fan out workers", "run X headless", or when a lifeos skill (overnight, timeboxed-iterating, workgraph, gauntlet, bughunt, dark-factory) needs to launch an agent CLI, or when a worker needs company context it should ask Viktor for (vktr).

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。

Firmware development for the Raspberry Pi Pico (original, RP2040 — dual Cortex-M0+ @ 133 MHz, 264 KB SRAM, 2 MB QSPI flash) — its 26 exposed GPIO, 4-channel 12-bit ADC, 16-channel PWM, 2× UART/I2C/SPI, PIO state machines, USB 1.1 CDC, VSYS/VBUS/SMPS power wiring, and the PlatformIO + arduino-pico (earlephilhower) setup around them. Use when working on this board, any Pico H or Pico clone, or a bare RP2040: project setup, platformio.ini, the earlephilhower-vs-Mbed core choice, pin mapping, USB CDC serial, analogRead/analogWrite behaviour, Wire/SPI defaults, battery power, EEPROM emulation, BOOTSEL/UF2 flashing, or debugging why something on the board does not work.

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。