Operate WorldEdit safely and efficiently for Minecraft 1.21.x server build/admin workflows. Covers selection mechanics, region operations, masks and patterns, clipboards and schematics, brushes and terraforming, undo/history safety, and practical runbooks for spawn edits, arena resets, block cleanup, and path shaping. Use for command-driven world operations, not plugin development.
npx skills add https://github.com/Jahrome907/minecraft-agent-skills --skill minecraft-worldedit-ops
This skill is for operating WorldEdit on live or staging servers.
It is not for writing plugin code that integrates with WorldEdit APIs.
Use when: the task is command-driven in-world editing with WorldEdit (selections, transforms, schematics, terrain shaping, rollback-safe workflows).Do not use when: the task is Java plugin implementation (minecraft-plugin-dev).Do not use when: the task is broad server deployment/tuning/proxy/backup strategy (minecraft-server-admin).Do not use when: the task is EssentialsX command/economy/moderation workflows (minecraft-essentials-ops).WorldEdit is commonly used on:
Operational best practice:
Compatibility note: stable WorldEdit 7.4.x targets current 1.21.x servers.
For Minecraft 26.1.x, check the WorldEdit release notes first and stage-test any
beta or pre-release build before using it on production worlds.
references/safety-checklists.md before large pastes, destructive replacements, or any edit where rollback discipline matters more than speed.Most common cuboid flow:
//wand
//pos1
//pos2
//size
Fast alternatives:
//hpos1
//hpos2
//chunk
//expand 20 up
//expand 20 down
Selection safety checks:
//size before //set, //replace, or //paste//distr to preview block composition before replacement//set stone
//replace stone andesite
//replace water air
//walls stone_bricks
//overlay grass_block
//smooth 3
Use masks to constrain scope:
//gmask #existing
//replace grass_block dirt
//gmask
Single-command mask approach:
//replace stone,andesite,diorite,granite smooth_stone
//replace ##leaves air
Pattern examples:
//set 70%stone,20%andesite,10%cobblestone
//replace dirt 60%coarse_dirt,40%podzol
//copy
//rotate 90
//flip east
//paste -a
Use -a when you want to skip air blocks during paste.
//schem save spawn-hub-v3
//schem list
//schem load spawn-hub-v3
//paste -a
Operational guidance:
arena-mid-2026-03-27)//brush sphere stone 4
//brush smooth 3
//brush raise 2
//brush lower 2
//mask #existing
Reset brush:
//none
Terraforming safety:
//undo checkpoints during long sessions//undo
//undo 5
//redo
Safety policy for production operations:
//undo immediately.Do not chain many destructive edits without intermediate verification.
Run //clearhistory only after the edit is accepted, a backup or schematic
checkpoint exists, and the rollback window is closed.
//size.//copy
//schem save spawn-before-refresh
//smooth 2
//schem save spawn-after-refresh
//schem load arena-pristine
//paste -a
Typical cleanup targets:
Example cleanup sequence:
//replace lava air
//replace water air
//replace ##leaves air
Always scope with selection/mask first to avoid map-wide accidental edits.
//set 50%dirt_path,30%coarse_dirt,20%gravel
stone_bricks, andesite) where elevation changes.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take jahrome907/minecraft-worldedit-ops from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.