Results for “continuous-batching”

Write, debug, and maintain Windows batch files (.bat/.cmd) for system administration, automation, and CLI tool development.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Composite skill — promote the long-lived `release` branch to `main`, cut a single version that batches every PR accumulated since the last tag, then clean up. Chains pr-merge-readiness (on release→main PR) → version-bump (semver from commits) → changelog-update (promote [Unreleased] → versioned) → ship (tag + GitHub release) → stale-branch cleanup → optional ship-it for deploy. Use this INSTEAD of releasing every small fix individually. Many small commits become one well-scoped release.

Analyzes AWS Batch compute environments, job queues, scheduling policies, and array jobs with parallel execution and anti-hallucination guardrails.

Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

Apply consistent photo adjustments across a set of images so they look like they were edited together, using Adobe tools for color, tone, cropping, and optional selective enhancements.

Applies the same change across multiple repositories in parallel using git worktrees, tests, and pull requests.

Generate pragmatic CI/CD pipelines from detected project stack signals, with fast baseline generation, repeatable checks, and environment-aware deployment stages.

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.

Automates CI/CD pipeline setup, quality gates, test runners, and deployment strategies for GitHub Actions.

Designs and implements CI/CD pipelines, automating build, test, and deployment processes with security scanning and deployment strategies.

Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers.

Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".

Data pipeline patterns — ETL/ELT, batch vs streaming, idempotency, orchestration. Use when designing a data pipeline, choosing between batch and streaming, implementing ingestion or transformation, setting up orchestration, or debugging pipeline failures.