「machine」の検索結果

Audit and rewrite prose so it stops reading as machine-generated. Use this skill when asked to remove AI-isms, clean up AI writing, edit a draft for AI tells, audit a README, changelog, release note, PR description, or blog post for machine-sounding prose, or make text sound less like AI. Supports a detect-only mode, a rewrite mode, and an edit-in-place mode, with optional voice and context profiles.

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。

Deploys anomaly detection for OT/ICS environments using machine learning on OT network baselines, physics-based process models, and Modbus/DNP3/OPC UA traffic analysis to flag deviations, rogue devices, and mismatches against historian data. Use for continuous OT monitoring, baselining deterministic SCADA polling, or investigating alerts from Nozomi Guardian/Dragos needing deeper protocol analysis.

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 secure OAuth 2.0 authorization flows, including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant, covering flow selection, PKCE implementation, token lifecycle management, and scope design per OAuth 2.1. Use when implementing or hardening OAuth 2.0 authentication/authorization for web, mobile, SPA, or machine-to-machine clients.

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。

Metaplex development on Solana — NFTs, tokens, compressed NFTs, candy machines, token launches, autonomous agents. Use when working with Token Metadata, Core, Bubblegum, Candy Machine, Genesis, Agent Registry, or the mplx CLI.

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。

Enables squad agents on different machines to share work via git-based task queuing

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。

Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

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。

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

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。

Build, run, debug and ship 3D city games in Godot 4 — procedurally generated cities (block grids, districts, weighted building typologies, MultiMesh batching, facade shaders, terrain, day/night, landmarks), first-person controllers, headless verification, Blender-generated .glb assets, rigged character and animation packs, real OpenStreetMap-derived cities, WebAssembly export, and cloud GPU streaming on GCP. Use this skill whenever the work involves Godot or a .gd/.tscn/.gdshader/project.godot file; whenever someone wants to start, set up, clone onto a new machine, or stand up a 3D game or city generator; whenever they mention procedural city, city generator, block grid, building typology, MultiMesh, facade shader, terrain heightmap, navmesh, Godot export templates, export presets, headless Godot, .glb pipeline, Blender asset generation, character rigs or borrowed animations; and whenever they want to deploy or share a game — web export, SharedArrayBuffer/COOP/COEP, Selkies, pixel streaming, GPU VM, or "let people play this without installing anything." Reach for it even on vague asks like "get this running", "why is my city slow", "my model faces backwards", "make it work on a cloud machine", or "set this up somewhere else too" when a Godot 3D project is in play.

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。

The machine has to stay one machine while the camera does all the work. Lock the vehicle part by part, then fill the runtime with a numbered cut list that moves the camera every second. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Cars and vehicles at speed; 中文触发词: 汽车与载具速度片, 汽车, 赛车, 摩托, 载具), or asks how published cases of this type were prompted. Read references/cases.md before drafting.

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Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.

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 the user asks to run, submit, monitor, or cancel a job on a remote machine over SSH — their own GPU/CPU server, a workstation, or a Slurm cluster ("the cluster", a login node, "my 3090 box", "the compute server"). Picks a saved machine, runs the work directly over SSH (or via Slurm when present), tracks it, and fetches results back into the workspace.

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Generate a co-author / collaborator handoff brief for a multi-author, multi-machine project — summarizing what changed since the last brief (git delta), the current state of each artifact (manuscript, analysis, slides), open questions, how to reproduce locally, and any restricted-data access steps. Use when user says "coauthor brief", "handoff brief", "bring my coauthor up to speed", "what changed since last week", "onboard a collaborator", "write a handoff for [name]", or before sending a co-author the repo. NOT a commit or a checkpoint — it is the cross-machine, cross-person summary `meta-governance.md` only partially covers.

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。

Screenplay format, page craft, the writing process and adaptation (剧本格式/写作流程/改编) — merged from Wendy Henson (spec format rules), Richard Walter (page craft, revision), Neill Hicks (selling vs shooting script, draft chain), Syd Field (master shot, card method, adaptation as original work), Robert McKee (step outline → treatment → screenplay), Julian Hoxter (format elements, adaptation four questions), Diamond & Weissman (one-page nine-step) and Eric Bork — plus an exact typographic grid, element conventions (V.O./O.S., MORE/CONT'D, dual dialogue), a machine-writable output contract (Fountain, with forced markers for Chinese), and Asian industry formats (Chinese 场号制 影视剧本格式, Japanese 柱・ト書き・セリフ / 決定稿). Use when formatting a spec script, choosing the output file format, deciding what goes in action lines, planning drafts and outlines, cutting pages, adapting a novel, play or true story, writing a Chinese TV/film or Japanese-style script page, or preparing a submission-ready draft.

Build game cameras that feel good — 2D follow with a deadzone, look-ahead, smoothing, and level-bounds clamping; 3D third-person orbit with collision and first-person look; plus multi-target framing and a shake hook. Engine-neutral techniques that pair with the engine's camera node and rigs like Unity Cinemachine or Godot Camera2D/PhantomCamera. Use when the user mentions camera follow, follow camera, deadzone, look-ahead, camera smoothing, camera bounds/ limits, third-person camera, orbit camera, first-person look, Cinemachine, or camera jitter.

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 NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state machine, behavior tree, blackboard, A*, navmesh, seek, or patrol/chase.

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First contact with Crabbox: run your repository's tests inside a disposable Docker or Podman container on your own machine, no account and no cloud spend, then stop the box. Use when someone asks what Crabbox is or how to try it, wants a throwaway sandbox for a repo with no crabbox.yaml yet, or is about to run crabbox init here; hand off to the crabbox skill for config that already exists, leased remote machines, jobs, secrets, or artifacts.

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。

Luma Dream Machine video generation: text-to-video and image-to-video, status polling, cancel, image upload. Trigger phrases: luma, dream machine, luma video, ray video.

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Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.

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Use when the user wants to train, compare, or export machine learning models using Classification Learner or Regression Learner — including opening the app, loading data, training models, evaluating metrics, comparing results, visualizing plots, testing on held-out data, exploring model interpretability, and exporting trained models. Programmatic access to Classification Learner and Regression Learner apps via AppController.

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Quick install of the Google Workspace CLI (gws) on an additional machine using existing OAuth credentials. Requires client_secret.json from a previous gws-setup. Use whenever the user wants to install gws on a new computer, reinstall after a fresh OS, configure a second workstation, or says 'install gws', 'gws on new machine', 'set up gws again'.

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。

Manage and run Power Automate Desktop (RPA) flows and machine groups. Use when the user asks about desktop flows, RPA, or machine groups.

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。

Provision and drive a `pu` box — an Incus container used as a clean Linux host for CI, builds, and evidence capture. Use when you need to run something on a fresh remote box instead of the user's machine: `nix run` a build, run CI against a real host, capture screenshots/video off-machine, or reproduce on a pristine environment. Covers create/connect/scp/destroy, running remote commands, copying artifacts back, and the no-egress failure mode. Triggers on "pu box", "spin up a box", "run this on a box", "ephemeral host", "pu create/connect/destroy".

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。