「process」の検索結果

Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).

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。

Execute via @babysitter. Use this skill when asked to babysit a task, do anything that is structured process-driven (even a loop) or whenever it is called explicitly. (babysit, babysitter, with a process, orchestrate, orchestrate a run, workflow, loop until, etc.)

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 any system where email content triggers actions — AI agent inboxes, automated support handlers, email-to-task pipelines, or any workflow processing untrusted inbound email. Always use this skill when the user wants to receive emails and act on them programmatically, even if they don't mention "agent" — the skill contains critical security patterns (sender allowlists, content filtering, sandboxed processing) that prevent untrusted email from controlling your system.

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。

Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.

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。

Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.

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。

MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.

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。

Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).

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。

Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.

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。

Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.

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。

WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.

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。

Download, resize, and remove backgrounds from product images at scale. Use when the user asks to "process product images", batch-download images from the schedule, strip backgrounds, or standardize product photos.

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。

Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).

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。

When the user needs to create SOPs, playbooks, runbooks, or other operational documentation that defines how a recurring process should be executed.

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。

Process construction payment applications. Validate schedule of values, calculate retainage, track billing status, and generate G702/G703 forms.

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。

Process and manage construction change orders. Track costs, approvals, and impact on schedule and budget.

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。

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

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 a Mermaid workflow diagram showing process steps, decisions, and state transitions. Use when documenting CI/CD pipelines or lifecycle processes.

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 processing, summarizing, or extracting insights from meeting notes in the Obsidian vault. Triggers on "summarize meetings", "process meetings", "meeting summaries", "extract from meetings", "meeting insights".

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。

Route GenAI Processors tasks to the right workflow guide.

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 Fairlearn to assess group fairness, compute disparity metrics, visualize subgroup performance, and mitigate unfairness with preprocessing, reductions, postprocessing, or adversarial learning.

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 richzhang/colorization PyTorch colorizers for automatic image colorization, Python API calls, preprocessing/postprocessing, and runtime troubleshooting.

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。

Address PR reviewer feedback by evaluating comments, proposing fixes, and executing after user approval. Use when the user wants to handle review comments on an existing PR, address PR feedback, fix PR comments, respond to reviewer suggestions, or process code review feedback. Triggers on "address PR feedback", "handle review comments", "fix PR comments", "respond to PR review", "process reviewer feedback", or when given a PR number/URL with intent to address its comments.

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。

On-site and remote vendor audit procedures per GDPR Article 28(3)(h). Covers audit planning, evidence collection methodologies, finding classification, remediation tracking, and audit report generation for processor compliance verification.

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。

Pre-contract vendor privacy due diligence per GDPR Article 28(1). Covers risk questionnaires, technical controls assessment, certification review, data flow analysis, and documented sufficiency decisions for processor engagement.

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。