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Store Data Structures

Checked
lobehub·vlatest·agents

Efficiently store and retrieve data using optimized lists, dictionaries, and sets within your LobeHub AI agent.

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 lobehub/lobehub --skill store-data-structures -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

lobehub

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 lobehub/lobehub at 0b22b7f532.

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

Commit
0b22b7f532
Manifest
.agents/skills/store-data-structures/SKILL.md
Scanned
2 files, 16 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

What it does

This skill provides guidance on structuring data within Zustand stores for optimal performance in LobeHub AI agents. It emphasizes separating data structures for list views (using arrays) and detail views (using maps), and utilizing types from @lobechat/types instead of database types. The skill defines distinct "Detail" and "List" types, with Detail types including comprehensive entity information and List types potentially incorporating computed UI fields.

When to use it

  • When designing data structures for list pages within your LobeHub AI agent.
  • When creating detail views that require access to complete entity information.
  • When optimizing the performance of your agent by using appropriate data structures for caching and display.
  • When needing to distinguish between lightweight list items and full detail entities.

Key capabilities

  • Separation of List and Detail Structures: Uses arrays for lists and maps (Record<string, Detail>) for details.
  • Type Definitions: Provides example type definitions for entities like AgentEvalBenchmark (Detail) and AgentEvalBenchmarkListItem (List).
  • Usage of @lobechat/types: Specifies the use of types from @lobechat/types instead of database-specific types.
  • Distinction between List and Detail Types: Allows for computed UI fields in list types.

Example prompts

  • "Show me how to structure a data type for displaying a list of benchmarks."
  • "What's the best way to cache detail pages using Zustand stores?"
  • "Can you give me an example of a lightweight item type for a document list?"

Tips & gotchas

  • Avoid mixing List and Detail types: They serve different purposes.
  • Don’t use database types directly: Always use the types defined in @lobechat/types.
  • Be mindful of "heavy" fields: Exclude them from list item types to improve performance.