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lazy-batch-cloud

Loop cloud state scripts to emit plan and execution steps per cycle.

Updated Aug 27, 2026
Search Tags:#automation#orchestration#cloud#pipeline#batch#sentinels
One-click install
npx skills add https://github.com/jacobrocks1212/claude-config --skill lazy-batch-cloud
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lazy-batch-cloud
Source: https://github.com/jacobrocks1212/claude-config/tree/main/repos/algobooth/.claude/skills/lazy-batch-cloud
Command: npx skills add https://github.com/jacobrocks1212/claude-config --skill lazy-batch-cloud

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cloud-based orchestration for AI pipelines that loops on the cloud state script, coordinating per-cycle planning and execution while deferring MCP-dependent steps to workstation when necessary.

Core Features & Use Cases

  • Cloud-mode batch orchestration: loops through lazy-state.py --cloud, spawning per-cycle work and halting on the same terminal conditions, but in a cloud environment without Tauri or MCP runtime.
  • Deferral and gating: defers MCP tests and related steps to the workstation flow, while maintaining sentinel-driven progress and recovery semantics.
  • Cloud-recovery and resilience: supports resume after container reclaim, reconciliation against git state, and max_cycles enforcement.

Quick Start

Launch the cloud variant with /lazy-batch-cloud and let it drive per-cycle state transitions until a terminal cloud condition or max cycles is reached.

Frequently Asked Questions about lazy-batch-cloud

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate batch AI pipelines in a cloud environment without local MCP runtimes?▼

Cloud-based batch orchestration loops through a cloud state script to emit plan and execution steps per cycle, deferring MCP-dependent tests to a local workstation to avoid blocking the autonomous AI workflow.

How does cloud batch orchestration handle container reclaim and resume for AI pipelines?▼

Cloud orchestration supports resume after container reclaim by reconciling against the git state and enforcing max_cycles, ensuring pipeline recovery and state continuity without manual intervention.

What are the hard constraints for autonomous cloud workflows transitioning through SPEC and PLAN phases?▼

Autonomous cloud workflows enforce max_cycles limits, restrict sentinel edits to allowed areas only, and halt on cloud-queue states or MCP test deferrals to maintain safe execution boundaries.

Can I run cloud-based batch pipelines without Tauri or MCP runtime dependencies?▼

Yes, the cloud variant of batch orchestration operates without Tauri or MCP runtime, continuing background tasks without blocking while deferring MCP-dependent steps to the workstation flow.

How do I start cloud-mode batch orchestration for autonomous AI workflows?▼

Launch the cloud variant to drive per-cycle state transitions through SPEC, PHASES, and PLAN stages until a terminal cloud condition or the maximum cycle limit is reached.

Why does my cloud batch pipeline halt on cloud-queue states during autonomous execution?▼

Cloud-queue states act as terminal conditions that halt the orchestration loop, ensuring pipeline safety when sentinel-driven progress encounters unrecoverable queue conflicts or gating failures.

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