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openrouter-caching-strategy skill

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/plugins/saas-packs/openrouter-pack/skills/openrouter-caching-strategy

This skill helps implement OpenRouter response caching to reduce latency and costs by using LRU and semantic caching strategies.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill openrouter-caching-strategy

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: openrouter-caching-strategy
description: |
  Implement response caching for OpenRouter efficiency. Use when optimizing costs or reducing latency for repeated queries. Trigger with phrases like 'openrouter cache', 'cache llm responses', 'openrouter redis', 'semantic caching'.
allowed-tools: Read, Write, Edit, Grep
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---

# Openrouter Caching Strategy

## Overview

This skill covers caching strategies from simple LRU caches to semantic similarity caching for intelligent response reuse.

## Prerequisites

- OpenRouter integration
- Caching infrastructure (Redis recommended for production)

## Instructions

Follow these steps to implement this skill:

1. **Verify Prerequisites**: Ensure all prerequisites listed above are met
2. **Review the Implementation**: Study the code examples and patterns below
3. **Adapt to Your Environment**: Modify configuration values for your setup
4. **Test the Integration**: Run the verification steps to confirm functionality
5. **Monitor in Production**: Set up appropriate logging and monitoring

## Output

Successful execution produces:
- Working OpenRouter integration
- Verified API connectivity
- Example responses demonstrating functionality

## Error Handling

See `{baseDir}/references/errors.md` for comprehensive error handling.

## Examples

See `{baseDir}/references/examples.md` for detailed examples.

## Resources

- [OpenRouter Documentation](https://openrouter.ai/docs)
- [OpenRouter Models](https://openrouter.ai/models)
- [OpenRouter API Reference](https://openrouter.ai/docs/api-reference)
- [OpenRouter Status](https://status.openrouter.ai)

Overview

This skill implements response caching strategies to reduce cost and latency when using OpenRouter. It covers simple in-memory LRU caches, Redis-backed caches for production, and semantic similarity caching to reuse relevant responses. Use it to avoid repeated calls for similar prompts and to improve throughput for high-volume workloads.

How this skill works

The skill inspects outgoing prompts and incoming model responses, deciding whether to store or retrieve a cached entry based on exact-match or semantic similarity. For production, it integrates with Redis to persist cached responses, TTLs, and eviction policies; for local testing it supports an LRU in-memory store. A similarity layer computes embeddings and uses vector or nearest-neighbor lookups to return semantically matching responses when confidence thresholds are met.

When to use it

When repeated or predictable prompts cause repeated OpenRouter calls and higher costs
To reduce response latency for frequently asked queries or UI actions
When you need a production-grade cache with persistence and eviction (use Redis)
During load spikes to prevent hitting rate limits or spiking compute costs
When implementing semantic reuse for paraphrased user inputs

Best practices

Start with an in-memory LRU for development, then move to Redis for production
Define sensible TTLs per prompt type to avoid serving stale information
Use embeddings and a confidence threshold to enable semantic caching safely
Log cache hits and misses and monitor hit rate, latency, and storage use
Store metadata (model, temperature, prompt hash) so cached responses remain valid

Example use cases

Caching FAQ responses for a customer support chatbot to cut cost and latency
Storing model completions for deterministic prompts in an automated pipeline
Semantic caching for paraphrased user queries in an assistant or knowledge app
Protecting an app from rate limits during marketing spikes by serving cached answers
A/B testing model parameters while reusing baseline completions to save budget

FAQ

Do I need Redis for this skill?

Redis is recommended for production for persistence and shared cache across instances; an in-memory LRU is fine for local development and testing.

How does semantic caching avoid returning wrong answers?

Semantic caching uses embedding similarity plus a configurable confidence threshold and stores model/temperature metadata so you only reuse responses when similarity and metadata match your safety rules.