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Query Caching Strategies

Checked
aj-geddes·vlatest·backend·MIT

Optimizes query performance by intelligently caching results based on frequency, recency, and relevance.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill query-caching-strategies -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/query-caching-strategies/SKILL.md
Scanned
9 files, 373 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

This skill enables AI agents to retrieve and reuse previously generated responses, significantly reducing token usage and improving response speed for repetitive tasks. It automatically manages cache keys based on input parameters and handles cache invalidation when underlying data changes.

When to use it

  • Repetitive Data Queries: Fetching static information like product prices, API documentation, or company policies that rarely change.
  • Long-Running Sessions: Maintaining context in extended conversations where the agent needs to reference earlier decisions without re-generating them.
  • Cost Optimization: Reducing API costs by preventing redundant calls to expensive external knowledge bases or LLMs for identical inputs.
  • Latency Reduction: Ensuring near-instant responses for common user questions that have already been processed and stored.

Key capabilities

  • Automatic key generation based on input variables and context.
  • Support for time-based expiration (TTL) to keep data fresh.
  • Conditional retrieval logic to decide when to fetch new data versus using cached results.
  • Integration hooks for external storage backends like Redis or in-memory stores.

Example prompts

  • "Retrieve the current stock price for Apple, checking the cache first before querying the live market API."
  • "Explain the onboarding process for new employees, ensuring you use the cached version if it hasn't been updated in the last 24 hours."
  • "Summarize the latest news articles about AI regulation, but only fetch new summaries if the cache is older than one hour."

Tips & gotchas

Ensure your cache invalidation strategy aligns with how frequently your source data updates; stale cached data can lead to inaccurate agent responses. Be mindful of memory limits when storing large context windows in local caches versus distributed systems.