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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Covers polished document output, tracked changes, comments, image updates, XML-level repairs, and safe conversion workflows.

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。

Spec-driven development lifecycle — EARS requirements, technical design docs, implementation tracking, and contribution guidelines. Use when planning features, defining requirements, or managing project lifecycle.

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 video, music, speech, or images with the operator's MiniMax Token Plan subscription through the codex-router media CLI. Use when the session runs a MiniMax custom (non-OpenAI) model (for example minimax-m3) with the MiniMax Token Plan provider connected, and the user explicitly asks to create a video, a song or music track, spoken audio, or an image. Do not use for reading or analyzing existing media.

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。

Asset Management Specialist IA — Expert en gestion d'actifs (inventory, depreciation, maintenance scheduling, barcodes)

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。

Expert en observabilité des agents IA (tracing, logging, suivi des coûts, latence, métriques qualité)

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。

Guide for optimizing MSBuild incremental builds. Only activate in MSBuild/.NET build context. USE FOR: builds slower than expected on subsequent runs, 'nothing changed but it rebuilds anyway', diagnosing why targets re-execute unnecessarily, fixing broken no-op builds. Covers 8 common causes: missing Inputs/Outputs on custom targets, volatile properties in output paths (timestamps/GUIDs), file writes outside tracked Outputs, missing FileWrites registration, glob changes, Visual Studio Fast Up-to-Date Check (FUTDC) issues. Key diagnostic: look for 'Building target completely' vs 'Skipping target' in binlog. DO NOT USE FOR: first-time build slowness (use build-perf-baseline), parallelism issues (use build-parallelism), evaluation-phase slowness (use eval-performance), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, search, target details); falls back to dotnet build /bl, binlog replay with diagnostic verbosity.

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。

Automate Sentry tasks via Rube MCP (Composio): manage issues/events, configure alerts, track releases, monitor projects and teams. Always search tools first for current schemas.

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。

Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.

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。

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

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。

MLflow, model versioning, experiment tracking, model registry, and production ML systems

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Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools.

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。

Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

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。

Scale, weight, line height, and tracking as a coherent system. Use when building type scales, auditing typography consistency, or diagnosing why text feels off.

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。

GPU monitoring with risk intelligence. Local + cloud fleet monitoring, health tracking, proactive alerts, and AI-powered fleet analytics.

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。

Orchestrates the dedicated Kinect wall harp implementation team. Use whenever the user asks to build, implement, continue, fix, QA, document, or ship the Kinect wall harp, Kinect v2 wall-depth harp, FreenectTD depth-blob hand tracking, projected wall strings, pluck synth harp, or this feature's Layer 1 tool/recipe/docs. Use this before the generic tdmcp-pipeline for this feature.

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Build a crew call sheet and a load-in / load-out plan for one show day from your event tracker or brief and your crew list, and send it to the crew only after your GO. Use for "call sheet", "crew call times", "load-in plan", "load-out schedule".

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。

Track build processing, find latest builds, and clean up old builds with asc. Use when managing build retention or waiting on processing.

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。

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

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When the user wants to track App Store chart rank changes, find top gainers and losers, detect breakout apps entering the top 100, or identify apps dropping out of charts. Also use when the user mentions "chart movers", "rank changes", "who's rising", "who's falling", "new chart entries", "top gainers", or "market shifts". For broader market overview, see market-pulse. For competitive keyword analysis, see competitor-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。

Access accelerometer, gyroscope, magnetometer, pedometer, and activity-recognition data using CoreMotion. Use when reading device sensor data, counting steps, detecting user activity (walking/running/driving), tracking altitude changes, or implementing motion-based interactions in iOS/watchOS apps.

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。

Implement, review, or improve maps and location features in iOS/macOS apps using MapKit and CoreLocation. Use when working with Map views, annotations, markers, polylines, user location tracking, geocoding, reverse geocoding, search/autocomplete, directions and routes, geofencing, region monitoring, CLLocationUpdate async streams, or location authorization flows. Also use when working with maps, coordinates, addresses, places, directions, distance calculations, or location-based features in Swift apps.

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。

Transfer app data between platforms using AppMigrationKit. Use when implementing one-time data migration from Android or other platforms to iOS, managing cross-platform transfer sessions with AppMigrationExtension, packaging and archiving user data for export, importing resources on the destination device, tracking transfer progress, handling migration errors, or building onboarding flows that import existing user data.

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。

Prepare for App Store review and prevent rejections. Covers App Store review guidelines, app rejection reasons, PrivacyInfo.xcprivacy privacy manifest requirements, required API reason codes, in-app purchase IAP and StoreKit rules, App Store Guidelines compliance, ATT App Tracking Transparency, EU DMA Digital Markets Act, HIG compliance checklist, app submission preparation, review preparation, metadata requirements, entitlements, widgets, and Live Activities review rules. Use when preparing for App Store submission, fixing rejection reasons, auditing privacy manifests, implementing ATT consent flow, configuring StoreKit IAP, or checking HIG 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。