「safety」の検索結果

Prequalify subcontractors based on safety, financial, and performance criteria.

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 construction site photos to track progress, detect safety issues, and compare against BIM models using computer vision.

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。

Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.

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。

Field operations in OpenConstructionERP: punch list, daily diary, HSE observations and task tracking on site. Use when automating construction site workflows (deficiencies, diaries, safety, site tasks).

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。

Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.

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。

Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.

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。

Aggregate and analyze IoT sensor data from construction sites. Collect data from multiple sensor types, detect anomalies, and trigger alerts for safety and quality monitoring.

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。

Monitor environmental conditions on construction sites. Track air quality, noise levels, vibration, dust, and weather to ensure compliance and worker safety.

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。

Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.

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 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.

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。

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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。

Classifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.

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 minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

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。

Operate and extend GeoNode geospatial content-management deployments, resource APIs, uploads, metadata/catalogues, GeoServer security, harvesting, and administration with explicit service and safety gates.

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 Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot operations.

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。

Shared discipline for every hunt-* skill: scope and authorization gating, the two-account rule, the confirmation gate that separates a real finding from a false positive, enumeration limits, stop conditions, marker discipline, wiki-first query and self-heal, FIND output, Deadends, and wiki distillation. ALWAYS LOADED alongside any hunt skill. Also trigger directly on "is this in scope", "is this a real bug", "how do I confirm this", "should I keep going", "how many IDs should I test", "what severity", "how do I report this", "I got someone else's data". Every hunt-* skill assumes this file; without it they run without their safety and quality layer.

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 building batch jobs, ETL pipelines, scheduled imports/exports, or any chunk-oriented bulk processing with Spring Batch. Covers the Spring Batch 6 / Boot 4 builder API, resourceless vs JDBC job repositories, restartability and idempotent job parameters, reader/writer thread-safety, fault tolerance, and chunk transaction boundaries.

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 annotating nullability in Spring Boot 4 / Spring Framework 7, migrating to JSpecify, integrating Kotlin, or enabling build-time null checks with NullAway.

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 annotating nullability in Spring Boot 3 / Spring Framework 6 code, integrating Kotlin, or adding static nullability checks without assuming Spring Framework 7 JSpecify defaults.

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 building batch jobs, ETL pipelines, scheduled imports/exports, or any chunk-oriented bulk processing with Spring Batch. Covers the Spring Batch 5 / Boot 3 builder API, restartability and idempotent job parameters, reader/writer thread-safety, fault tolerance, and chunk transaction boundaries.

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。

Autonomous AI code generation safety guardrail register: static AST analysis, forbidden import filters, and zero-day vulnerability checks.

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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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 AI-focused SRE incident response practices for LLM outages, degraded quality, runaway cost events, and safety regressions.

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。

Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and code execution strategies with safety-first dry runs.

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。