3,170 件 ・ 関連度順
概要と使いどころ
Audit game narrative systems for technical quality -- branching dialogue trees, state/flag tracking, quest systems, choice-consequence mapping, localization pipelines, voice-over integration, and cinematic scripting..
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 a production-grade KYC / AML / sanctions screening pipeline for customer onboarding, transaction monitoring, and ongoing review. Triggers: "KYC", "AML", "OFAC", "sanctions screening", "PEP".
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。
Audit e-discovery and litigation document review systems -- data collection pipelines (PST, MBOX, SharePoint, Slack), document processing (OCR via Tesseract/ABBYY, metadata extraction, deduplication), Technology Assisted Review (TAR 1.0/2.0/CAL with recall/precision tracking).
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 medical billing and revenue cycle management software including claims processing pipelines, EDI transaction handling (837P/837I/835/270/271/276/277), ICD-10 and CPT code validation with NCCI edit checking, payer rules engine configuration, denial management and appeal 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。
Audit an emergency resource management system for crisis readiness. Evaluates inventory tracking accuracy, deployment request-to-arrival pipeline, logistics and route optimization, supply chain resilience, inter-agency resource sharing (NIMS/EDXL), staging area management.
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 disaster prediction and early warning systems — model accuracy for flood, earthquake, wildfire, hurricane, and tsunami hazards, data pipeline reliability from sensor networks and satellite feeds, alert distribution latency and channel coverage.
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。
Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science 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。
Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining 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。
Analyze fraud detection systems including rule engines, ML scoring models, real-time transaction monitoring, alert triage workflows, false positive management, SAR/CTR regulatory reporting, adversarial robustness testing, and adaptive retraining pipelines for payment fraud, account takeover, identity theft, and AML 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。
Audit a grant management system for proposal workflow efficiency, deadline tracking, budget-narrative alignment, outcome reporting, compliance readiness, and win rate optimization. Use when reviewing nonprofit grant software, building a grants CRM, analyzing proposal pipelines, or evaluating funder reporting 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。
Analyze manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system 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。
Audit precision agriculture and crop management software for yield prediction model accuracy, soil analysis integration, irrigation optimization algorithms, pest and disease detection pipelines, satellite and drone imagery processing, weather data integration.
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。
Analyzes SIEM alert pipelines for rule optimization, alert fatigue reduction, criticality scoring, asset-based prioritization, and correlation rule design using NIST CSF and detection engineering principles..
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。
Parse natural-language quantities, units, dates, and ranges ("5'11\"", "1.5 cups", "72 in to cm", "three days ago", "between 5 and 10 kg", "it's hot") into canonical, validated values with issue codes and spans — and humanize them back. Use when a form input, LLM tool schema, MCP handler, or import pipeline takes free-text measurements/units/dates. Covers Standard Schema fields (quantityField/dateField/lingoObject) for the AI SDK and MCP, the headless <lingo-input>, unit conversion, and format/humanize round-trips. Zero-dependency TypeScript, deterministic.
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。
**MEASUREMENT SKILL** — Benchmark framework for measuring skill effectiveness. A/B tests agent outputs with and without a target skill, scoring correctness, completeness, and pattern adherence. USE FOR: validating skill quality, comparing before/after skill changes, proving skill ROI with data. DO NOT USE FOR: general testing, unit tests, CI 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。
Deep cultural adaptation of an already translated blog post for one target market: local examples, CTAs, legal references, statistic sources, and formality (Sie/du, tu/vous). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when the user says "localize blog", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar" and a translation already exists. Not for translating (use blog-translate), auditing a folder of translations (use blog-locale-audit), or the full write-translate-localize pipeline (use blog-multilingual).
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。
One-command multilingual blog creation. Writes a blog post, translates it into user-specified languages, applies cultural adaptation, and emits hreflang tags, sitemap entries, and a CMS-ready language map. The complete write-to-publish pipeline for international content. Orchestrates blog-write, blog-translate, blog-localize, and (optionally) seo-hreflang. Use when user says "multilingual blog", "blog multilingual", "write in multiple languages", "international blog", "mehrsprachiger Blog", "blog multilingue", "blog multilingue", "create blog in German and French".
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。
Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT trigger for heavy production needs — use video-podcast-maker for those.
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。
AI 视频生成全流程：通过 6 个阶段（剧本→角色/场景设计→分镜→参考图→视频生成→后期剪辑）将用户想法转化为完整视频。支持临时工作台（单独调用 LLM、VLM、文生图、图生图、视频生成）和一次性短流程 Pipeline（文艺短视频、动作迁移、数字人口播）。触发词：视频生成、AI视频、AIGC、创作视频、制作视频、AI画图、文艺短视频、静态短视频、动作迁移、数字人口播。
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。
Run Yuzu's Codex governance review pipeline on a commit range with slash-name parity to Claude `/governance`. Use when the user says `/governance <range>`, "full governance", or asks for the multi-gate security/docs/domain/ops review on commits or a PR.
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。
Run or plan Yuzu's `/test` pre-commit and pre-push validation pipeline with quick, default, full, instructions, and quarantine modes. Use when the user says `/test`, `/test --quick`, `/test --full`, asks for the full test gate, or needs the Yuzu test-runs DB workflow.
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 working on the SAM3 plus Sapiens2 pose estimation pipeline, batch GPU processing, or VRAM and throughput issues.
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。
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.
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 Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
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。