How much faster will my Claude Code sessions be?

Most users see a 20-40% reduction in execution time. The savings scale with the complexity of the workflow; longer 'epic' tasks benefit more from cached searches and documentation.

Does this skill require manual configuration?

No, it is designed for transparent operation. Once active, it automatically detects repeated operations and manages the cache without requiring manual intervention.

Will using this skill provide stale data?

No. The skill uses smart invalidation logic, including file modification time (mtime) checks and specific TTLs, to ensure that if a file or source changes, the cache is automatically bypassed.

What exactly does the skill cache?

It caches expensive operations including file reads (15min TTL), npm package info (60min), grep search results (30min), token counts, and web searches (15min).

Intelligent Caching Strategy

Name: Intelligent Caching Strategy
Author: marcusgoll

bymarcusgoll

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Productivity & Workflow

Accelerates Claude Code workflows by intelligently caching redundant file reads, search results, and API calls to reduce execution time by up to 40%.

About

The Intelligent Caching Strategy skill optimizes Claude Code's performance by eliminating redundant operations across complex workflow phases. By implementing a smart multi-layer cache for expensive tasks—such as codebase searches, documentation reads, and network requests—it significantly reduces latency without altering task outcomes. The skill utilizes sophisticated invalidation logic, including Time-To-Live (TTL) expirations and file modification (mtime) checks, ensuring that developers get the speed of cached data with the reliability of fresh results. This is particularly effective for large-scale projects and multi-phase 'Spec-Driven Development' cycles.

Key Features

Performance monitoring with hit-rate and time-savings tracking
Network request caching for npm metadata and web search results
18 GitHub stars
Smart caching for file I/O, grep searches, and glob patterns
Token count persistence to avoid redundant LLM cost calculations
Automatic invalidation via mtime checks and configurable TTLs

Use Cases

Accelerating multi-phase feature implementations (/feature) by skipping repeated documentation reads
Optimizing large codebase audits where grep and search operations are frequently repeated
Reducing network latency during dependency analysis and external API documentation lookups

About

Key Features

Performance monitoring with hit-rate and time-savings tracking
Network request caching for npm metadata and web search results
18 GitHub stars
Smart caching for file I/O, grep searches, and glob patterns
Token count persistence to avoid redundant LLM cost calculations
Automatic invalidation via mtime checks and configurable TTLs

Use Cases

Accelerating multi-phase feature implementations (/feature) by skipping repeated documentation reads
Optimizing large codebase audits where grep and search operations are frequently repeated
Reducing network latency during dependency analysis and external API documentation lookups