「windows」の検索結果

Guidelines for writing platform-specific code in Luma. Use when adding or modifying code that behaves differently on Unix vs Windows, including shell commands, file paths, process spawning, or platform-specific tests.

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 LLM context windows deliberately — budgeting, prioritization, summarization, and retrieval instead of dumping everything in. Use when prompts grow long, costs rise, or the model loses track of key details.

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 a production-grade Vehicle Routing Problem (VRP) solver using Google OR-Tools — supports CVRP (capacity), VRPTW (time windows), VRPPD (pickup + delivery), multi-depot, multi-trip, heterogeneous fleet, driver hours-of-service (HOS) compliance.

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。

Extract a structured abstract from a commercial lease PDF — tenant/landlord identification, premises description + RSF/USF, lease term + commencement/expiration, renewal options (count, notice windows, fair-market rent reset), base rent schedule + escalations (fixed %, CPI-linked.

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。

FALLBACK ONLY — do not invoke unless you have already ruled out (1) a native API (Gmail API, GitHub API, Slack API …), (2) a CLI (git, gh, aws, npm, curl …), (3) direct file editing, and (4) existing browser automation (Playwright, Puppeteer). Only when all four are unavailable or have already failed should you use this skill. It gives AI agents a cursor and a keyboard on a real desktop — the last mile when the only remaining surface is a GUI. Concretely: use it when an earlier attempt via API, CLI, or direct file edit has failed and the user says things like "open X", "click Send", "type this in Word", "read what is on my screen", "do this in Outlook", "drive the Figma UI", "control my desktop", "automate this workflow", "fill out this form", or "copy text between apps". Works on Windows, macOS, and Linux with any LLM that can call functions (Claude, GPT, Gemini, Llama, Kimi, Ollama) over MCP — stdio for editor hosts (Claude Code, Cursor, Windsurf, Zed) or HTTP for daemons and dashboards.

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。

Cut a Yuzu release end-to-end. Runs preflight checks, validates compose-file version pins, pushes the tag, monitors the release workflow until artifacts publish, troubleshoots known failure modes (artifact download bugs, version mismatch, Windows signing, macOS notarization), verifies the GitHub Releases page has every expected asset including the Compose Wizard zip, and produces a release record. Use when the user says "/release vX.Y.Z" or asks to cut a release.

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。

Configure, compile, and test Yuzu with the repo's canonical Meson entrypoints and per-OS build directories. Use when validating code changes, selecting test suites, reconfiguring a build directory, or recovering from host/builddir mismatches on Linux, macOS, or Windows.

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。

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like "we need to upgrade our clusters" or "plan our next GKE maintenance" or "our upgrade is stuck." Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills 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。

Configure FSL Shifts, ShiftTemplates, ShiftPatterns, and Crew ServiceResources — availability windows layered on Operating Hours, bulk shift generation via ShiftPattern + ShiftPatternEntry, and the two Crew models (Static vs. Shell/Dynamic). NOT for setting up the resources themselves — types, skills, ResourcePreference — use admin/fsl-resource-management. NOT for capping how many hours or appointments a resource can take — use admin/fsl-capacity-planning.

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。

Handle Chinese mojibake in CMD, .bat/.cmd files, Windows logs, and native command output by using PowerShell to read raw bytes as GBK/CP936, capture command output with an explicit decoder, and convert it to UTF-8/Unicode. Use when text contains 锟斤拷, mojibake, or replacement characters and the source encoding must be verified.

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

Cross-platform path handling and command 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。

.NET timezone handling guidance for C# applications. Use when working with TimeZoneInfo, DateTimeOffset, NodaTime, UTC conversion, daylight saving time, scheduling across timezones, cross-platform Windows/IANA timezone IDs, or when a .NET user needs the timezone for a city, address, region, or country and copy-paste-ready C# code.

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。

Give an AI agent its own real browser over MCP tool calls - launch, navigate, click, fill, screenshot, extract text, run JS - with a kernel-level real-device fingerprint and a persistent profile, so the session stays logged in between runs and pages see one coherent device instead of a headless build. No Playwright or SDK code to write. Use when an agent should operate a site itself, when a computer-use / browser-use setup needs a captured real fingerprint rather than a synthetic one, when agent sessions keep losing their login, or when comparing hosted agent-browser services. Also for 'MCP browser', 'browser MCP server', 'let my agent browse the web', 'agent browser control', 'browser-use MCP', 'computer use browser', 'Browserbase alternative', 'Steel browser alternative', 'headless browser detected'. Node (npx) or Python; Windows x64, macOS Intel + Apple Silicon, Linux x64 / arm64. SDK and REST reference is anti-detect-browser; account isolation is multi-account-isolation.

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 used when the user asks to "attack Active Directory", "exploit AD", "Kerberoasting", "DCSync", "pass-the-hash", "BloodHound enumeration", "Golden Ticket", "Silver Ticket", "AS-REP roasting", "NTLM relay", or needs guidance on Windows domain penetration testing.

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 Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

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。

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

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 this skill whenever the user asks to generate, edit, inpaint, restyle, or create bitmap images through OpenAI's Responses API image_generation tool from Codex. This skill calls the Responses API, saves generated base64 image results to files, uses no npm or pip dependencies, works on Linux/macOS/Windows with either Node.js 18+ or Python 3, and reuses Codex's configured API base URL, model, and key instead of asking the user for credentials.

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 daemon/background processes that start on boot, run continuously, and manage their own lifecycle. Covers macOS launchd (plist files, agents vs daemons), Linux systemd (unit files), Windows services, process supervision, logging, health checks, graceful shutdown, auto-restart, and AI-powered daemons that manage LLM API connections and rate limits. Activate on: "daemon", "background process", "launchd", "systemd", "service file", "plist", "launch agent", "launch daemon", "auto-start", "always running", "process supervisor", "pm2", "background service", "boot service", "AI daemon", "long-running process". NOT for: container orchestration (use devops-automator), cron jobs that run and exit (use task-scheduler), web server deployment (use backend-architect).

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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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 decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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。

Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

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。

Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.

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 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

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。