「ker」の検索結果

Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.

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Turn Chinese classical poems and ci into coherent vertical Chinese-art videos with poem-driven scene grouping, GPT ImageGen stills, Docker-only Gemini I2V, retained model-generated ambience, Gemini sparkle-watermark cleanup, brush-calligraphy captions revealed character by character, optional local BGM mixing, stitching, and final-frame QA. Supports verse-driven variation across ink landscape, gongbi bird-and-flower, colored figure-and-horse painting, blue-green landscape, xuan paper, silk, and related Chinese visual languages. Use when users ask for 古诗词动态视频、诗词逐句或两句一景、国风视频、毛笔字逐字出现、整首诗拼接成片，or want the established 月落乌啼霜满天 workflow applied to another poem.

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Use when you need to CAPTURE / PRODUCE XProf (jax.profiler) profiling artifacts from sgl-jax (SGLang on JAX/TPU) for later performance analysis: driving a live server (/start_profile, stage-separated prefill/decode), offline bench_one_batch --profile, or kernel microbench. This skill produces the trace files; it does not analyze them.

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Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.

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Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.

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Adversarially fact-check DOCS.md files against the actual source code, verifying every concrete claim rather than trusting structure checks. Use when the user asks to audit docs, verify documentation accuracy, check whether docs are still true, or on a scheduled documentation review. This is the semantic layer the mechanical checkers cannot cover.

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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.

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Track regulatory compliance for construction projects. Monitor permits, certifications, inspections, and regulatory requirements with automated alerts and reporting.

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Track and manage construction lien waivers. Monitor conditional and unconditional waivers, ensure compliance before payments, and prevent lien exposure.

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Track construction retainage/retention amounts. Monitor held amounts by subcontractor, track release conditions, and manage retainage billing.

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Convert voice recordings to structured construction reports. Field workers speak, AI transcribes and formats. Supports daily reports, safety observations, progress updates.

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Build Telegram bots for construction field workers. Real-time reporting, photo uploads, task assignments, progress tracking. Integrate with n8n for automated workflows.

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Check BIM model consistency: naming conventions, parameter completeness, spatial relationships, and data integrity across model elements.

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Check BIM data against IDS (Information Delivery Specification). Validate model information requirements and compliance.

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Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.

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Check data compliance with construction standards. Validate data against ISO 19650, IFC, COBie, UniFormat standards.

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Track material orders, deliveries, and inventory on construction sites. Monitor lead times, delivery status, and stock levels.

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Track construction submittals through the review process. Manage approvals, revisions, and compliance.

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Track material procurement from requisition to delivery. Monitor lead times, vendors, and costs.

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Automatically generate daily construction reports from field data, worker inputs, weather, and progress photos. Creates professional PDF reports.

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 subcontractor payments, lien waivers, and compliance. Manage payment schedules and 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。

Track actual vs planned productivity using CWICR norms. Calculate productivity rates, identify variances, and generate performance reports.

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 and manage construction warranties. Monitor expiration dates, claims, and manufacturer 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。

Track as-built documentation and record drawings. Monitor submission status, manage revisions, and ensure completeness for handover.

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。