How much can this skill reduce token usage?

Depending on the hook, users can see reductions between 39% and 82% by removing verbose explanations and using structured, concise JSON formats.

Does this skill help with context window management?

Yes, by reducing the 'tax' of hook instructions to under 5,000 tokens per hook, it leaves more room in the context window for actual project code and logic.

Which specific CCPM hooks are optimized?

This skill provides optimized versions for the Smart Agent Selector (UserPromptSubmit), the TDD Enforcer (PreToolUse), and the Quality Gate (Stop Hook).

How does the cached agent discovery work?

It implements a 5-minute Time-To-Live (TTL) cache for the agent list, allowing Claude to skip expensive file system scans and reducing discovery time from seconds to milliseconds.

What is the primary benefit of hook optimization?

It significantly reduces execution latency to under 5 seconds and slashes token usage, making Claude Code more responsive and cost-effective during development.

CCPM Hook Optimizer

Name: CCPM Hook Optimizer
Author: duongdev

byduongdev

•

生产力与工作流

Optimizes Claude Code Project Management hooks to ensure high performance and minimal token consumption.

关于

The CCPM Hook Optimization skill is designed to streamline the execution of Claude Code hooks, specifically targeting the Smart Agent Selector, TDD Enforcer, and Quality Gate. By implementing caching strategies, progressive disclosure, and conditional logic, this skill helps developers achieve sub-5-second execution times and significant token savings—reducing prompt sizes by up to 82%. It is essential for teams using CCPM who want to maintain a responsive AI development workflow without exhausting token budgets or context windows.

主要功能

Cached agent discovery logic reducing latency from 2,000ms to <100ms
Conditional fast-path logic to skip unnecessary hook processing
5 GitHub stars
Sub-5-second execution targets for all automation hooks
Token-efficient templates for Agent Selection and Quality Gates
Performance benchmarking and profiling tools for custom hooks

使用场景

Improving responsiveness in large-scale repositories with complex agent workflows
Enforcing Test-Driven Development (TDD) and quality gates without slowing down the developer
Reducing API costs by minimizing repetitive token overhead in every message

关于

主要功能

Cached agent discovery logic reducing latency from 2,000ms to <100ms
Conditional fast-path logic to skip unnecessary hook processing
5 GitHub stars
Sub-5-second execution targets for all automation hooks
Token-efficient templates for Agent Selection and Quality Gates
Performance benchmarking and profiling tools for custom hooks

使用场景

Improving responsiveness in large-scale repositories with complex agent workflows
Enforcing Test-Driven Development (TDD) and quality gates without slowing down the developer
Reducing API costs by minimizing repetitive token overhead in every message