「pipe flow」の検索結果

Audit a media production studio or post-production facility. Analyzes facility scheduling and utilization, equipment lifecycle tracking, editorial and VFX pipelines, color grading and finishing workflows, digital asset management (MAM/DAM), IMF/MXF format 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。

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 laboratory automation systems -- LIMS architecture, instrument connectivity (SiLA 2, OPC-UA, serial drivers), sample tracking and chain of custody, protocol workflow engines, data acquisition pipelines, and regulatory compliance (21 CFR Part 11 electronic records/signatures.

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。

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。

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。

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。

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。

Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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。

Teaches the fundamentals of Google Cloud Build (GCB). Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.

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。

BA (Business Analyst) agent. Analyzes requirements and produces 4 artifact files: requirements.md, user-stories.md, acceptance-criteria.md, business-rules.md. Use --srs to load from SRS workflow artifacts (spec.md / brainstorm.md). Part of the Virtual Team Skill pipeline.

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。

Eliciting and documenting a sales process before it is built in Salesforce: stage sequencing, entry/exit criteria per stage, win/loss analysis requirements, and stage transition rules. Trigger keywords: sales process design, stage discovery, entry criteria, exit criteria, stage gate, win/loss categorisation, sales methodology, pipeline stages, opportunity stage mapping, stage ladder, probability ladder, forecast category mapping, required fields per stage, stage swim lane, discovery workshop, handoff brief. NOT for configuring OpportunityStage, Sales Processes or Path in Setup — use admin/opportunity-management. NOT for cross-functional swim-lane maps — use admin/process-flow-as-is-to-be.

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 this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory, AI disclosure. NOT for the MLOps pipeline or Policy-as-Code engine architecture — use architect/ai-governance-architecture. NOT for Trust Layer technical configuration — use agentforce/einstein-trust-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。

GitHub Actions workflow patterns for React Native iOS simulator and Android emulator cloud builds with downloadable artifacts. Use when setting up CI build pipelines or downloading GitHub Actions artifacts via gh CLI and GitHub API.

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。

Game art principles. Visual style selection, asset pipeline, animation 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 this skill when the user is working with an Aspire distributed application and needs to operate the AppHost or its resources through the Aspire CLI: start, restart, stop, or wait on the app; inspect resources, logs, traces, docs, or health; add integrations; manage secrets or config; publish, deploy, or rerun a named pipeline step; initialize Aspire in an existing app; recover missing `.modules` files in a TypeScript AppHost; discover the right frontend URL for Playwright from Aspire state; expose custom dashboard/resource commands; or understand unfamiliar Aspire AppHost APIs in C# or TypeScript. Use it even if they describe the task in terms of an AppHost, resources, dashboard, existing app bootstrap, missing generated modules, Playwright URL discovery, C# API understanding, or local distributed app workflow without explicitly naming Aspire. Do not use it for non-Aspire .NET apps, container-only repos with no AppHost, or ordinary build and test 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。

Autonomous comprehensive review. Once-through pipeline over /review-health, /review-arch, /review-security, /review-perf, /review-a11y, /review-test, /review-release. Operator-configurable ticket creation at startup — when ON, auto-approves sub-skill ticket proposals per the orchestrator-family contract; when OFF, uniformly declines and surfaces findings in the consolidated report. Auto-detects sub-skills that do not apply (no web content → skip /review-a11y; no tests → skip /review-test).

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 comprehensive refactoring. Three-phase pipeline — Phase 1 /refactor (tactical, loops internally to convergence) → Phase 2 loop of /review-arch + /implement-batch until architectural findings converge below a severity floor (default HIGH+) → Phase 3 /refactor (catches tactical issues introduced by restructuring). Auto-approves /review-arch ticket proposals; pulls the andon cord on contested findings, breaking-change proposals, repeated implementation failure, or Phase-2 hard-cap exhaustion (5 architectural iterations).

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。

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.

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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Evaluate an incoming workflow upgrade — a skill, MCP server, subagent, library, external service, prompt, or pattern — through one disciplined pipeline before adopting it. Security is a blocking gate. Records every verdict so the same thing is never evaluated twice. Use when considering a new tool from a post, repo, or recommendation. Triggers — "vet this", "should we adopt", "is this safe to install", "evaluate this skill/MCP", "what do you think of this tool".

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。

Single-task pipeline: plan → test → build → review (ship separately). Use for a feature or scoped fix. NOT for green-field builds (mk:autobuild) or auto-orchestration (mk:workflow-orchestrator).

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。