「loading」の検索結果

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for firmware images, partition tables, boot chains, update packages, extracted filesystems, embedded configs, and device-facing trust boundaries. Use when the user asks to unpack firmware, map partition layout, inspect bootloader or init chains, recover update keys or credentials, trace config loading, or explain how a device surface reaches the decisive artifact. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for source maps, build manifests, chunk registries, emitted bundles, obfuscated loader flow, and frontend runtime recovery. Use when the user asks to reconstruct served JavaScript structure, inspect source maps or chunk maps, trace bundle loading, recover hidden routes or APIs from emitted assets, or explain runtime behavior from built frontend artifacts. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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 microinteractions, motion design, transitions, and user feedback patterns. Use when adding polish to UI interactions, implementing loading states, or creating delightful user experiences.

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。

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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。

Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat detection.

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。

Writes and tunes Falco rule syntax for container escape detection - conditions, macros, lists, priorities, and output fields - covering host filesystem mounts, sensitive host path access, kernel module loading, and privileged capability abuse, including how to drive down false positives. Use when authoring or tuning a specific Falco rule for breakout behaviour, or triaging a noisy escape-related Falco alert. Keywords: Falco rule, macro, list, condition, priority, falco_rules.local.yaml, tuning, false positive. Do not use for deploying and operating Falco itself - use detecting-container-runtime-threats-with-falco; for tool-agnostic escape signals use detecting-container-escape-attempts.

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。

Parses the Windows Amcache.hve registry hive with Eric Zimmerman's AmcacheParser and Timeline Explorer to extract evidence of program execution, application installation, and driver loading, including SHA-1 hash correlation with threat intel and timeline reconstruction. Use for Amcache forensics, program execution evidence gathering, or application compatibility cache investigations in DFIR 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。

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

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。

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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。

Convert an attached Word document, presentation, spreadsheet, OpenDocument file, RTF, EPUB, CSV, or text-based PDF into local Markdown. Use when a message supplies a local attachment path that must be read without uploading it to an external parser.

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。

Add local office-document-to-Markdown conversion to NanoClaw agent containers with the pinned Firecrawl AnyDoc CLI. Use when agents need to read attached Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, or text-based PDF files without uploading them to a hosted parser.

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。

Morning preparation. Calendar lookahead, meeting context loading, open threads from yesterday, active task review. Extends briefing with actionable prep.

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 compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "is this offer competitive", "model this equity grant", or when uploading comp data to find outliers and retention risks.

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 needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private/age-restricted videos.

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。

Multi-agent board meeting protocol for strategic decisions. Runs a structured 6-phase deliberation: context loading, independent C-suite contributions (isolated, no cross-pollination), critic analysis, synthesis, founder review, and decision extraction. Use when the user invokes /cs:boardroom, calls a board meeting, or wants structured multi-perspective executive deliberation on a strategic question.

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。

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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。

Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.

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。

Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).

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。

Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using natural language.

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。

Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate 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。

Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).

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。

Validates a project's Google Mobile Ads (GMA) SDK integration for iOS, Android, or Unity projects. Use when conducting a full pre-launch audit of an app that integrates GMA SDK or when validating any individual GMA SDK integration checks, such as when validating ad unit IDs and ad formats, SKAdNetwork IDs, mediation adapter SDK version compatibility, or ad preloading.

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 CONNECT Microsoft Teams (`kortix channels connect --platform teams`, prints a tenant-admin consent URL) and how to answer in Teams as a teammate. Covers the live Adaptive Card stream (`teams step` with --detail/--output/--source, `teams send` to finalize the answer), sending and downloading files (the consent-card upload flow + `teams download`), reading teams/channels/members through connector calls, asking the user, and the tone the bot should use. Load this when the turn is triggered from Teams (the prompt mentions a Teams tenant/conversation, or `$MS_TEAMS_CONVERSATION_ID` is set in the env), or when the user asks how to do anything in Teams.

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。