home / skills / jeremylongshore / claude-code-plugins-plus-skills / batch-inference-pipeline
This skill guides and accelerates batch inference pipeline setup with production-ready code, configurations, and validation aligned to ML deployment best
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill batch-inference-pipelineReview the files below or copy the command above to add this skill to your agents.
---
name: "batch-inference-pipeline"
description: |
Execute batch inference pipeline operations. Auto-activating skill for ML Deployment.
Triggers on: batch inference pipeline, batch inference pipeline
Part of the ML Deployment skill category. Use when working with batch inference pipeline functionality. Trigger with phrases like "batch inference pipeline", "batch pipeline", "batch".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---
# Batch Inference Pipeline
## Overview
This skill provides automated assistance for batch inference pipeline tasks within the ML Deployment domain.
## When to Use
This skill activates automatically when you:
- Mention "batch inference pipeline" in your request
- Ask about batch inference pipeline patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.
## Instructions
1. Provides step-by-step guidance for batch inference pipeline
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards
## Examples
**Example: Basic Usage**
Request: "Help me with batch inference pipeline"
Result: Provides step-by-step guidance and generates appropriate configurations
## Prerequisites
- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts
## Output
- Generated configurations and code
- Best practice recommendations
- Validation results
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
## Resources
- Official documentation for related tools
- Best practices guides
- Community examples and tutorials
## Related Skills
Part of the **ML Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production
This skill automates guidance and code generation for batch inference pipeline operations in ML deployment. It is an auto-activating assistant focused on production-ready patterns for running large-scale, scheduled, or ad-hoc batch model inference. The skill emphasizes reliability, validation, and deployment best practices to help move pipelines from prototype to production quickly.
The skill inspects your request for batch inference intent and provides step-by-step guidance, configuration templates, and runnable code snippets for common platforms (cloud, on-prem, containerized). It generates pipeline definitions, scheduling configurations, resource sizing recommendations, and validation checks. It also highlights monitoring and error-handling patterns to keep pipelines robust in production.
What inputs are required to generate a pipeline configuration?
Provide model artifact location, input data location and schema, desired runtime environment, scheduling cadence, and resource constraints.
Can the skill produce code for specific platforms?
Yes. It can generate templates for common targets (Kubernetes jobs, Airflow/DAGs, cloud batch services, or simple containerized scripts) when you specify the target platform.
How does it help with validation and monitoring?
It adds validation steps, sample-based checks, metrics collection points, and suggested alerting rules to detect failures and data drift early.