How does this skill help reduce costs?

It routes LLM requests through the Claude CLI and SDK patterns that utilize your existing Claude subscription, rather than incurring separate per-token API charges.

What programming languages are supported?

The patterns primarily use Bash for hook orchestration and Python for the SDK integration logic.

Will running LLM calls slow down my hooks?

No, this skill specifically includes 'Background Agent' patterns that return an immediate response (0.01s) while processing the LLM task in the background.

What is Hook SDK Integration?

It is a set of implementation patterns used to call LLMs from script-based hooks (like Git or Bash hooks) using a specialized SDK, allowing for advanced automation.

LLM Hook SDK Integration

Name: LLM Hook SDK Integration
Author: chkim-su

bychkim-su

Productivity & Workflow

Orchestrates LLM invocations within development hooks to automate background tasks and optimize subscription-based costs.

About

This skill provides specialized patterns for integrating Large Language Model (LLM) calls directly into system or Git hooks using the u-llm-sdk. It enables developers to implement non-blocking background agents, execute type-safe CLI prompts, and leverage Claude subscription usage to reduce external API expenses. By utilizing these patterns, teams can build sophisticated automation workflows that run seamlessly within their development environment without interrupting the coding process or incurring per-token billing.

Key Features

Non-blocking background agent patterns for immediate hook returns
Subscription-aware logic to avoid additional per-token API costs
0 GitHub stars
Support for multiple execution modes including direct CLI and local LLMs
Type-safe Python SDK configuration for robust LLM interactions
Cost-optimized LLM calls leveraging existing Claude CLI subscriptions

Use Cases

Triggering intelligent code analysis or documentation updates during Git commit hooks
Building non-blocking CLI background agents for complex multi-step tasks
Optimizing automation costs by routing LLM calls through subscription-tier models

About

Key Features

Non-blocking background agent patterns for immediate hook returns
Subscription-aware logic to avoid additional per-token API costs
0 GitHub stars
Support for multiple execution modes including direct CLI and local LLMs
Type-safe Python SDK configuration for robust LLM interactions
Cost-optimized LLM calls leveraging existing Claude CLI subscriptions

Use Cases

Triggering intelligent code analysis or documentation updates during Git commit hooks
Building non-blocking CLI background agents for complex multi-step tasks
Optimizing automation costs by routing LLM calls through subscription-tier models