What is the benefit of the service layer wrapper?

The service layer follows the Single Responsibility Principle, allowing your core processing functions to remain 'pure' and focused on data transformation while the wrapper handles the 'impure' caching logic.

Why use content hashing instead of file paths for caching?

File paths are brittle; if a file is renamed or moved, path-based caching fails. Content hashing ensures the cache remains valid as long as the file content is identical, regardless of its location.

Does this pattern handle large files efficiently?

Yes, the pattern utilizes chunked reading (e.g., 64KB chunks) to compute SHA-256 hashes, preventing high memory consumption even when processing very large files.

How does cache invalidation work in this pattern?

Invalidation is automatic and implicit. Because the cache key is the hash of the content, any change to the file results in a different hash, which naturally creates a cache miss and triggers re-processing.

Content Hash Cache Pattern

Name: Content Hash Cache Pattern
Author: Fabio29T

byFabio29T

0•

开发者工具

Optimizes file processing pipelines by caching expensive results using SHA-256 content-based hashing.

This skill implements a robust caching strategy for computationally expensive file operations such as PDF parsing, OCR, and image analysis. By using SHA-256 hashes of file contents rather than file paths, it ensures cache persistence even when files are moved or renamed, and automatically invalidates entries when content changes. The pattern emphasizes the Single Responsibility Principle by separating caching logic from core processing functions through a service-layer wrapper, providing O(1) lookup performance and efficient memory management for large files.

主要功能

01O(1) lookup performance using hash-based file storage

020 GitHub stars

03Automatic cache invalidation upon any file content modification

04SHA-256 content-based hashing for path-independent caching

05Service layer architecture that preserves pure processing functions

06Memory-efficient chunked hashing for processing large files

使用场景

01Implementing persistent CLI tool caching with --cache/--no-cache options

02Reducing latency in repetitive OCR and text extraction workflows

03Building high-performance data ingestion pipelines for PDF or image datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fabio29t/everything-claude content-hash-cache-pattern

For use in Claude.ai and ChatGPT

主要功能

01O(1) lookup performance using hash-based file storage

020 GitHub stars

03Automatic cache invalidation upon any file content modification

04SHA-256 content-based hashing for path-independent caching

05Service layer architecture that preserves pure processing functions

06Memory-efficient chunked hashing for processing large files

使用场景

01Implementing persistent CLI tool caching with --cache/--no-cache options

02Reducing latency in repetitive OCR and text extraction workflows

03Building high-performance data ingestion pipelines for PDF or image datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fabio29t/everything-claude content-hash-cache-pattern

For use in Claude.ai and ChatGPT