auto-next-batch

Identify the next 100-card batch and compute target and rank ranges.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/Tomoda826/mtg-goldfisher --skill auto-next-batch
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: auto-next-batch
Source: https://github.com/Tomoda826/mtg-goldfisher/tree/main/.claude/skills/auto-next-batch
Command: npx skills add https://github.com/Tomoda826/mtg-goldfisher --skill auto-next-batch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Automated Scenario Pipeline Manager identifies the next 100-card batch, extracts RAW mechanics only, and orchestrates AI-driven scenario design to replace manual generation with a quality-first automated workflow.

Core Features & Use Cases

  • Automatic batch discovery and target/rank calculation from results/scenarios files.
  • Safe chunking, context safety checks, and manifest tracking to ensure reliable, auditable outputs.
  • Coordinated AI design with parallel agents and post-run validation to produce consistent scenario sets.

Quick Start

Identify the next 100-card batch and start the end-to-end AI-driven design pipeline.

Frequently Asked Questions about auto-next-batch

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

FAQPage Schema
How do I automate batch processing for AI-driven scenario design?

Automated scenario design works by identifying the next 100-card batch, extracting RAW mechanics, and chunking cards into 10-card groups. It validates contexts and produces a manifest to orchestrate parallel AI agents for consistent scenario set generation.

How do I safely chunk card batches for automated scenario generation?

Safely chunking card batches for automated scenario generation requires dividing the 100-card batch into 10-card groups and performing context safety checks. This process ensures reliable, auditable outputs before producing a manifest for final AI design.

Can I use parallel agents to generate scenario sets from existing card datasets?

Yes, you can use parallel agents to generate scenario sets from existing card datasets. The workflow computes target and rank ranges from your results/scenarios files and coordinates parallel AI agents with post-run validation to produce consistent outputs.

What is the best way to extract RAW mechanics from a local scenarios dataset?

Extracting RAW mechanics from a local scenarios dataset is handled by an automated pipeline that identifies the next 100-card batch and isolates the mechanics. This replaces manual extraction and prepares the data directly for coordinated AI design.

Does automated scenario design require manifest tracking for quality checks?

Yes, automated scenario design requires manifest tracking for quality checks to ensure reliable and auditable outputs. The manifest tracks the 10-card chunking process and validates contexts before final AI design execution.