How does this improve code maintainability?

It uses a Service Layer wrapper that follows the Single Responsibility Principle. This keeps your core logic (like text extraction) pure and unaware of the caching infrastructure, making it easier to test and reuse.

Does this skill handle large files efficiently?

Yes, the implementation uses chunked reading (64KB blocks) to compute hashes, preventing memory exhaustion even when processing very large files.

What happens if a cache file becomes corrupted?

The pattern is designed to handle corruption gracefully by treating invalid or unreadable JSON cache files as a standard cache miss, triggering a fresh processing run.

Why use content hashes instead of file paths for caching?

Paths are fragile; if you move or rename a file, a path-based cache fails. Content hashes (SHA-256) identify a file by its unique data, meaning the cache remains valid regardless of where the file is stored.

When should I not use this caching pattern?

Avoid this pattern for real-time data that changes constantly or when the processing result depends on external configurations/parameters rather than just the file content itself.

Content-Hash Cache Pattern

Name: Content-Hash Cache Pattern
Author: lyxjack

bylyxjack

•

開発者ツール

Optimizes expensive file processing tasks by implementing content-based SHA-256 caching for path-independent result retrieval.

The Content-Hash Cache Pattern provides a standardized approach to caching the results of high-latency file operations such as PDF parsing, image analysis, and text extraction. By using SHA-256 content hashes instead of file paths as cache keys, this skill ensures that cache hits occur even when files are moved or renamed, while providing automatic invalidation whenever file content changes. It promotes a clean architectural separation between pure processing functions and the caching service layer, making it ideal for building robust data pipelines and CLI tools that require efficient resource management.

主な機能

011 GitHub stars

02Memory-efficient chunked hashing for large file support

03Automatic cache invalidation on file content modification

04Service layer separation to keep processing functions pure

05Path-independent caching using SHA-256 content hashes

06Simplified O(1) file-based storage requiring no central index

ユースケース

01Adding --cache/--no-cache functionality to developer CLI tools

02Optimizing batch document analysis where files frequently change locations

03Building high-performance PDF or OCR processing pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lyxjack/toolbox content-hash-cache-pattern

For use in Claude.ai and ChatGPT

主な機能

011 GitHub stars

02Memory-efficient chunked hashing for large file support

03Automatic cache invalidation on file content modification

04Service layer separation to keep processing functions pure

05Path-independent caching using SHA-256 content hashes

06Simplified O(1) file-based storage requiring no central index

ユースケース

01Adding --cache/--no-cache functionality to developer CLI tools

02Optimizing batch document analysis where files frequently change locations

03Building high-performance PDF or OCR processing pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lyxjack/toolbox content-hash-cache-pattern

For use in Claude.ai and ChatGPT