How do I install the Vastai Core Workflow A skill?

Run npx skillmds@latest add micsapp/vastai-core-workflow-a in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Vastai Core Workflow A free to use?

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Who published Vastai Core Workflow A?

micsapp (@micsapp) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Vastai Core Workflow A by micsapp — Agent Skill

Name: Vastai Core Workflow A
Rating: 4.666666666666667 (3 reviews)
Author: micsapp

Vastai Core Workflow A

Execute Vast.ai primary workflow: Core Workflow A. Use when implementing primary use case, building main features, or core integration tasks. Trigger with phrases like "vastai main workflow", "primary task with vastai".

micsapp Updated Sep 8, 2026 3 repo stars

File contents

Vast.ai Core Workflow A

Overview

Primary money-path workflow for Vast.ai. This is the most common use case. Vast.ai is a marketplace for renting GPU compute from individual hosts and data centers at prices significantly lower than hyperscaler providers. It is commonly used for training machine learning models, running large-scale inference, and rendering workloads where cost efficiency matters more than guaranteed SLA uptime.

Prerequisites

Completed vastai-install-auth setup
Understanding of Vast.ai core concepts
Valid API credentials configured

Instructions

Step 1: Initialize

Authenticate with the Vast.ai API and search for available instances that match your requirements. Filter by GPU model, VRAM size, geographical region, reliability score, and price per hour. Sort offers by the metric most relevant to your workload — price for budget-conscious batch jobs, reliability score for long-running training runs, or interconnect bandwidth for distributed training setups.

// Step 1 implementation

Step 2: Execute

Rent the selected instance by creating an offer via the API. Specify the Docker image containing your workload environment, the startup command, and any environment variables or volume mounts needed. Monitor the instance status until it reaches the running state, then connect via SSH or the Jupyter interface to verify the environment is configured correctly before beginning your compute job.

// Step 2 implementation

Step 3: Finalize

When your job completes, copy all output artifacts (model checkpoints, logs, result files) to persistent storage before destroying the instance. Destroying the instance promptly stops billing. Review the actual cost against your budget estimate and note any performance characteristics that should influence future instance selection.

// Step 3 implementation

Output

Completed Core Workflow A execution
GPU instance provisioned and job executed successfully
Output artifacts saved to persistent storage
Success confirmation or error details if provisioning or execution failed

Error Handling

Error	Cause	Solution
Error 1	Cause	Solution
Error 2	Cause	Solution

Examples

Complete Workflow

// Complete workflow example

Common Variations

Variation 1: Description
Variation 2: Description

Resources

Next Steps

For secondary workflow, see vastai-core-workflow-b.

---name: vastai-core-workflow-adescription: Execute Vast.ai primary workflow: Core Workflow A. Use when implementing primary use case, building main features, or core integration tasks. Trigger with phrases like "vastai main workflow", "primary task with vastai".license: MIT---# Vast.ai Core Workflow A## OverviewPrimary money-path workflow for Vast.ai. This is the most common use case. Vast.ai is a marketplace for renting GPU compute from individual hosts and data centers at prices significantly lower than hyperscaler providers. It is commonly used for training machine learning models, running large-scale inference, and rendering workloads where cost efficiency matters more than guaranteed SLA uptime.## Prerequisites- Completed `vastai-install-auth` setup- Understanding of Vast.ai core concepts- Valid API credentials configured## Instructions### Step 1: InitializeAuthenticate with the Vast.ai API and search for available instances that match your requirements. Filter by GPU model, VRAM size, geographical region, reliability score, and price per hour. Sort offers by the metric most relevant to your workload — price for budget-conscious batch jobs, reliability score for long-running training runs, or interconnect bandwidth for distributed training setups.```typescript// Step 1 implementation```### Step 2: ExecuteRent the selected instance by creating an offer via the API. Specify the Docker image containing your workload environment, the startup command, and any environment variables or volume mounts needed. Monitor the instance status until it reaches the running state, then connect via SSH or the Jupyter interface to verify the environment is configured correctly before beginning your compute job.```typescript// Step 2 implementation```### Step 3: FinalizeWhen your job completes, copy all output artifacts (model checkpoints, logs, result files) to persistent storage before destroying the instance. Destroying the instance promptly stops billing. Review the actual cost against your budget estimate and note any performance characteristics that should influence future instance selection.```typescript// Step 3 implementation```## Output- Completed Core Workflow A execution- GPU instance provisioned and job executed successfully- Output artifacts saved to persistent storage- Success confirmation or error details if provisioning or execution failed## Error Handling| Error | Cause | Solution ||-------|-------|----------|| Error 1 | Cause | Solution || Error 2 | Cause | Solution |## Examples### Complete Workflow```typescript// Complete workflow example```### Common Variations- Variation 1: Description- Variation 2: Description## Resources- [Vast.ai Documentation](https://docs.vastai.com)- [Vast.ai API Reference](https://docs.vastai.com/api)## Next StepsFor secondary workflow, see `vastai-core-workflow-b`.

Frequently asked questions