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swarm-ai-safetyOFFICIAL
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seed

Initialize SWARM batch processing jobs by setting up queues and extraction tasks.

4|Updated Feb 16, 2026
Search Tags:#task management#swarm#queue#processing#experiment#claim numbering
One-click install
npx skills add https://github.com/swarm-ai-safety/swarm-artifacts --skill seed-swarm-ai-safety
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seed
Source: https://github.com/swarm-ai-safety/swarm-artifacts/tree/main/.claude/skills/seed
Command: npx skills add https://github.com/swarm-ai-safety/swarm-artifacts --skill seed-swarm-ai-safety

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the setup and queuing of SWARM experiment runs, ensuring a smooth start to pipeline processing and consistent claim numbering.

Core Features & Use Cases

  • Run Initialization: Prepares new SWARM experiment runs for processing by reading run data and calculating necessary metadata.
  • Claim Numbering: Establishes a global, sequential numbering system for claims across all runs.
  • Task Queuing: Creates and adds extraction tasks to the processing queue.
  • Use Case: When a new experiment run is completed, use this Skill to automatically prepare it for data extraction and analysis, ensuring it's correctly added to the workflow.

Quick Start

Use the seed skill to prepare all unprocessed runs in the runs/ directory for processing.

Frequently Asked Questions about seed

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

FAQPage Schema
How do I initialize batch processing for SWARM experiment runs?▼

To initialize SWARM experiment processing, use the seed skill to read run folders, calculate the next claim number, and generate task files to add runs to the processing queue.

What is the best way to set up sequential claim numbering for experiment data?▼

The best way to establish sequential claim numbering is to use the seed skill, which calculates a global, sequential numbering system for claims across all SWARM experiment runs during initialization.

How does task queuing work for new experiment runs?▼

Task queuing works by reading unprocessed runs, determining metadata, and creating extraction tasks that are added directly to the processing queue for pipeline execution.

Can I prepare multiple unprocessed runs in the runs directory for extraction?▼

Yes, you can prepare multiple unprocessed runs by triggering the seed skill, which scans the runs directory and initializes batch processing jobs for all available experiment data.

Do I need any dependencies to start SWARM pipeline processing?▼

No dependencies are required to start SWARM pipeline processing, as the seed skill operates independently to read run folders and generate the necessary queue entries.

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