content-hash-cache

Cache expensive inputs and model results using SHA-256 keyed entries.

Updated Feb 9, 2026

Search Tags:#performance #llm #memory #cache #embedding #hash #sha256

Ready to run

npx skills add https://github.com/Xza85hrf/claude-code-agent-kit --skill content-hash-cache-xza85hrf

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: content-hash-cache
Source: https://github.com/Xza85hrf/claude-code-agent-kit/tree/main/.claude/skills/architecture/content-hash-cache
Command: npx skills add https://github.com/Xza85hrf/claude-code-agent-kit --skill content-hash-cache-xza85hrf

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deterministic caching with content hashes for long-term memory and expensive computations to avoid recomputation and speed up repeated tasks.

Core Features & Use Cases

Hash-based multi-layer cache with memory, file, and persistent storage
TTL-based expiration per category (embeddings, API calls, analysis)
Safe, atomic writes and eviction (LRU)

Quick Start

Run the content-hash-cache pattern to store a computed result from a given input and retrieve it on future runs.

Frequently Asked Questions about content-hash-cache

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

FAQPage Schema

How do I cache expensive LLM API calls and embeddings to avoid recomputation?▼

You can cache expensive LLM API calls and embeddings using SHA-256 keyed entries. This deterministic caching method stores computed results across memory, file, and persistent storage layers to prevent recomputation in repeated tasks.

What is the best way to implement deterministic caching for multi-model reasoning workflows?▼

Deterministic caching for multi-model reasoning uses content hashes to store and retrieve model results. A multi-layer cache applies TTL rules and LRU eviction to manage memory, file, and persistent storage effectively across long-running tasks.

Does content hash caching support TTL expiration and LRU eviction for static analysis?▼

Content hash caching supports TTL-based expiration per category and LRU eviction for static analysis. It applies safe, atomic writes across memory, file, and persistent storage layers to manage cached data efficiently.

Can I use SHA-256 hashing to cache results across different storage layers?▼

You can use SHA-256 hashing to cache results across memory, file, and persistent storage layers. This multi-layer cache approach ensures deterministic retrieval of expensive computations while applying TTL rules for data freshness.

Why should I use content hash caching for long-running tasks instead of standard memory cache?▼

Content hash caching provides deterministic retrieval for long-running tasks that standard memory cache lacks. By using SHA-256 keyed entries with TTL rules and LRU eviction, it safely persists expensive computations across multiple storage layers.