smithery/jeremylongshore

batch-inference-pipeline

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". '

Installation

$ npx skills add smithery/jeremylongshore --skill batch-inference-pipeline

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT
CompatibilityDesigned for Claude Code
Allowed toolsRead, Write, Edit, Bash(cmd:*), Grep
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,301 B
  • docs SUMMARY.md 368 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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