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jeffallan/ml-pipeline

Name: jeffallan/ml-pipeline
Author: Community

Was ist ml-pipeline?

Diese Fähigkeit hilft Entwicklern und MLOps-Ingenieuren beim Entwurf und der Implementierung vollständiger, produktionsreifer ML-Pipeline-Infrastruktur. Sie unterstützt die Konfiguration von Experiment-Tracking mit MLflow oder Weights & Biases, die Erstellung von Kubeflow- oder Airflow-DAGs für die Trainingsorchestrierung und den Aufbau von Feature-Stores mit Feast. Zudem automatisiert sie Modellregistrierung, Nachschulungs- und Validierungsworkflows und bietet nahtlose Integration mit Data-Versioning-Tools wie DVC sowie Hyperparameter-Tuning. Die Fähigkeit ist plattformunabhängig konzipiert und funktioniert mit gängigen MLOps-Tools wie Kubeflow, Airflow, MLflow oder Prefect. Ein besonderer Fokus liegt auf der Schaffung robuster, skalierbarer und wiederholbarer Workflows, die den gesamten ML-Modelllebenszyklus abdecken – von der Datenversionierung über das Experiment-Tracking bis hin zur Produktionsbereitstellung und kontinuierlichen Modellaktualisierung. Der Benutzer gibt typischerweise die gewünschten Tool-Stacks und architektonischen Präferenzen an, woraufhin die Fähigkeit detaillierte Konfigurationsdateien, Pipeline-Definitionen und Automatisierungsskripte generiert.

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Part ofDesign & UI

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Dokumentation

Was macht ml-pipeline?

Entwirft und implementiert produktionsreife ML-Pipeline-Infrastruktur: Konfiguriert Experiment-Tracking mit MLflow oder Weights & Biases, erstellt Kubeflow- oder Airflow-DAGs für die Trainingsorchestrierung, baut Feature-Store-Schemata mit Feast auf, stellt Modellregister bereit und automatisiert Nachschulungs- und Validierungsworkflows. Verwenden Sie dies beim Aufbau von ML-Pipelines, der Orchestrierung von Trainingsworkflows, der Automatisierung des Modelllebenszyklus, der Implementierung von Feature-Stores, der Verwaltung von Experiment-Tracking-Systemen, der Einrichtung von DVC für Datenversionierung, der Hyperparameter-Optimierung oder der Konfiguration von MLOps-Tools wie Kubeflow, Airflow, MLflow oder Prefect.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

jeffallan/angular-architect

Generates Angular 17+ standalone components, configures advanced routing with lazy loading and guards, implements NgRx state management, applies RxJS patterns, and optimizes bundle performance. Use when building Angular 17+ applications with standalone components or signals, setting up NgRx stores, establishing RxJS reactive patterns, performance tuning, or writing Angular tests for enterprise apps.

jeffallan/api-designer

Use when designing REST or GraphQL APIs, creating OpenAPI specifications, or planning API architecture. Invoke for resource modeling, versioning strategies, pagination patterns, error handling standards.

jeffallan/architecture-designer

Use when designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.

jeffallan/atlassian-mcp

Integrates with Atlassian products to manage project tracking and documentation via MCP protocol. Use when querying Jira issues with JQL filters, creating and updating tickets with custom fields, searching or editing Confluence pages with CQL, managing sprints and backlogs, setting up MCP server authentication, syncing documentation, or debugging Atlassian API integrations.

jeffallan/chaos-engineer

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.

jeffallan/cli-developer

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

jeffallan/cloud-architect

Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost optimization, disaster recovery, landing zones, security architecture, serverless design.

jeffallan/code-documenter

Generates, formats, and validates technical documentation — including docstrings, OpenAPI/Swagger specs, JSDoc annotations, doc portals, and user guides. Use when adding docstrings to functions or classes, creating API documentation, building documentation sites, or writing tutorials and user guides. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, getting started guides.

jeffallan/code-reviewer

Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.

jeffallan/cpp-pro

Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems techniques. Use when building or refactoring C++ code requiring concepts, ranges, coroutines, SIMD optimization, or careful memory management — or when addressing performance bottlenecks, concurrency issues, and build system configuration with CMake.

jeffallan/csharp-developer

Use when building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.

jeffallan/database-optimizer

Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.

jeffallan/debugging-wizard

Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause analysis.

jeffallan/devops-engineer

Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.

jeffallan/django-expert

Use when building Django web applications or REST APIs with Django REST Framework. Invoke when working with settings.py, models.py, manage.py, or any Django project file. Creates Django models with proper indexes, optimizes ORM queries using select_related/prefetch_related, builds DRF serializers and viewsets, and configures JWT authentication. Trigger terms: Django, DRF, Django REST Framework, Django ORM, Django model, serializer, viewset, Python web.

jeffallan/dotnet-core-expert

Use when building .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.

jeffallan/embedded-systems

Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

jeffallan/fastapi-expert

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.

jeffallan/feature-forge

Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists. Use when defining new features, gathering requirements, or writing specifications. Invoke for feature definition, requirements gathering, user stories, EARS format specs, PRDs, acceptance criteria, or requirement matrices.

jeffallan/fine-tuning-expert

Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.