content-hash-cache-pattern

Cache file processing results using SHA-256 content hashes.

1|Updated Mar 6, 2026
One-click install
npx skills add https://github.com/khetansarvesh/ai_skills_repo --skill content-hash-cache-pattern-khetansarvesh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: content-hash-cache-pattern
Source: https://github.com/khetansarvesh/ai_skills_repo/tree/main/skills/content-hash-cache-pattern
Command: npx skills add https://github.com/khetansarvesh/ai_skills_repo --skill content-hash-cache-pattern-khetansarvesh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents slow, expensive file processing from repeating across runs by caching results based on what the file content actually is rather than where it lives in your filesystem.

Core Features & Use Cases

  • Content-hash cache keys: Uses SHA-256 of file bytes so renames and moves keep cache hits while content changes trigger automatic invalidation.
  • Simple file-based storage: Stores each cached entry as a {hash}.json file for O(1) lookup without needing an index.
  • Service-layer separation (SRP): Keeps your original processing function pure while a wrapper handles cache check, extraction, and cache write.
  • Graceful corruption handling: Treats corrupted cache files as cache misses so the pipeline can re-process safely.

Quick Start

Implement a compute_file_hash function that streams SHA-256 over the file contents, then wrap your pure extract_text (or similar) function with a extract_with_cache service that checks and writes {hash}.json entries based on --cache/--no-cache.

Frequently Asked Questions about content-hash-cache-pattern

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I cache file processing results in Python to avoid re-parsing the same documents?

You can cache file processing results by generating a SHA-256 hash of the file content and storing the extracted output as a `{hash}.json` file. This allows subsequent runs to perform an O(1) lookup and skip expensive parsing if the hash already exists.

Does file content hashing handle cache invalidation when documents are renamed or moved?

Yes, content hashing handles renames and moves gracefully. Because the cache key is derived from the SHA-256 hash of the file bytes rather than the file path, renamed files will still result in cache hits, while any content changes trigger automatic cache invalidation.

What is the best way to cache PDF and image extraction results across multiple pipeline runs?

The best way is to use a service-layer wrapper that checks a file-based cache before calling your pure extraction function. It checks for a `{hash}.json` entry, returns the cached result if it exists, and writes new results to the cache after processing.

How do I keep my file processing function pure while adding a caching layer?

You keep the processing function pure by wrapping it with a separate service layer. This service handles the cache checking, file extraction, and cache writing, ensuring the underlying processing function remains focused solely on data extraction.

What happens if a cached file becomes corrupted during file processing?

Corrupted cache files are treated as cache misses. This graceful corruption handling ensures the pipeline safely re-processes the original file and overwrites the damaged cache entry rather than failing or returning bad data.

How do I compute hashes for large files without running out of memory?

You compute hashes for large files using chunked hashing. By streaming the file contents through the SHA-256 algorithm in chunks, you can generate the content hash without loading the entire file into memory.