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Agent Skill · 研究检索

batching-and-caching(批处理和缓存)

batching-and-caching 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

batching-and-caching 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 7,916。

总安装 17,705周安装 653GitHub Stars 公开资料未说明下载 9,062核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:batching-and-caching(批处理和缓存)
来源仓库:https://github.com/andrueandersoncs/claude-skill-effect-ts
仓库路径:skills/batching-and-caching
安装命令:
npx skills add andrueandersoncs/claude-skill-effect-ts --skill "batching-and-caching"
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add andrueandersoncs/claude-skill-effect-ts --skill "batching-and-caching"

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

batching-and-caching 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。主要功能是发现并安装 AI 代理的技能。

通过 github 安装,命令为 npx skills add andrueandersoncs/claude-skill-effect-ts --skill "batching-and-caching"。建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

可结合来源仓库和原始 README 继续核验具体用法。

来源仓库内路径:skills/batching-and-caching

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add andrueandersoncs/claude-skill-effect-ts --skill "batching-and-cac…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

batching-and-caching 怎么安装?

在终端执行 npx skills add andrueandersoncs/claude-skill-effect-ts --skill "batching-and-caching"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

batching-and-caching 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

batching-and-caching 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

batching-and-caching 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

batching-and-caching 用的人多吗?

公开数据是累计安装 17,705 次,近一周安装 653 次,下载 9,062 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

batching-and-caching 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
batching-and-caching

批处理和缓存 · 本页

17,705—需要联网MIT未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Batching and Caching in Effect

Overview

Effect provides automatic optimization for API calls:

  • Batching - Combine multiple requests into single API calls
  • Caching - Avoid redundant requests with smart caching
  • Deduplication - Prevent duplicate concurrent requests

This solves the N+1 query problem automatically.

The Problem: N+1 Queries

const program = Effect.gen(function* () {
  const todos = yield* getTodos()

  const owners = yield* Effect.forEach(
    todos,
    (todo) => getUserById(todo.ownerId),
    { concurrency: "unbounded" }
  )
})

Effect's batching transforms this into optimized batch calls.

Request-Based Batching

Step 1: Define Request Types

import { Request } from "effect"

// Define request shape
interface GetUserById extends Request.Request<User, UserNotFound> {
  readonly _tag: "GetUserById"
  readonly id: number
}

// Create tagged constructor
const GetUserById = Request.tagged<GetUserById>("GetUserById")

Step 2: Create Resolver

import { RequestResolver, Effect } from "effect"

// Batched resolver - handles multiple requests at once
const GetUserByIdResolver = RequestResolver.makeBatched(
  (requests: ReadonlyArray<GetUserById>) =>
    Effect.gen(function* () {
      // Single batch API call
      const users = yield* Effect.tryPromise(() =>
        fetch("/api/users/batch", {
          method: "POST",
          body: JSON.stringify({ ids: requests.map((r) => r.id) })
        }).then((res) => res.json())
      )

      // Complete each request with its result
      yield* Effect.forEach(requests, (request, index) =>
        Request.completeEffect(
          request,
          Effect.succeed(users[index])
        )
      )
    })
)

Step 3: Define Query

const getUserById = (id: number) =>
  Effect.request(GetUserById({ id }), GetUserByIdResolver)

Step 4: Use with Automatic Batching

const program = Effect.gen(function* () {
  const todos = yield* getTodos()

  const owners = yield* Effect.forEach(
    todos,
    (todo) => getUserById(todo.ownerId),
    { concurrency: "unbounded" }
  )
})

Resolver Types

Standard Resolver (No Batching)

const SingleUserResolver = RequestResolver.fromEffect(
  (request: GetUserById) =>
    Effect.tryPromise(() =>
      fetch(`/api/users/${request.id}`).then((r) => r.json())
    )
)

Batched Resolver

const BatchedUserResolver = RequestResolver.makeBatched(
  (requests: ReadonlyArray<GetUserById>) =>
    // Handle all requests in one call
    batchFetch(requests)
)

Resolver with Context

const UserResolverWithContext = RequestResolver.makeBatched(
  (requests: ReadonlyArray<GetUserById>) =>
    Effect.gen(function* () {
      // Access services from context
      const httpClient = yield* HttpClient
      const logger = yield* Logger

      yield* logger.info(`Batching ${requests.length} user requests`)

      return yield* httpClient.post("/api/users/batch", {
        ids: requests.map((r) => r.id)
      })
    })
)

// Provide context to resolver
const ContextualResolver = UserResolverWithContext.pipe(
  RequestResolver.provideContext(context)
)

Caching

Effect.cached - Memoize Effect Result

import { Effect } from "effect"

const fetchConfig = Effect.promise(() =>
  fetch("/api/config").then((r) => r.json())
)

const cachedConfig = yield* Effect.cached(fetchConfig)

const config1 = yield* cachedConfig
const config2 = yield* cachedConfig

Effect.cachedWithTTL - Time-Based Expiry

const cachedUser = yield* Effect.cachedWithTTL(
  fetchCurrentUser,
  "5 minutes"
)

const user1 = yield* cachedUser
yield* Effect.sleep("6 minutes")
const user2 = yield* cachedUser

Effect.cachedInvalidateWithTTL - Manual Invalidation

const [cachedUser, invalidate] = yield* Effect.cachedInvalidateWithTTL(
  fetchCurrentUser,
  "5 minutes"
)

const user = yield* cachedUser
yield* invalidate
const freshUser = yield* cachedUser

Cache Service

For more control, use the Cache service:

import { Cache } from "effect"

const program = Effect.gen(function* () {
  const cache = yield* Cache.make({
    capacity: 100,
    timeToLive: "10 minutes",
    lookup: (userId: string) => fetchUser(userId)
  })

  const user1 = yield* cache.get("user-1")
  const user2 = yield* cache.get("user-1")

  const isCached = yield* cache.contains("user-1")

  yield* cache.invalidate("user-1")

  const stats = yield* cache.cacheStats
})

Request Caching

Requests are automatically cached within a query context:

const program = Effect.gen(function* () {
  const user1 = yield* getUserById(1)
  const user2 = yield* getUserById(1)

  const user3 = yield* getUserById(2)
})

Disabling Request Caching

const noCaching = getUserById(1).pipe(
  Effect.withRequestCaching(false)
)

Custom Cache for Requests

const customCache = yield* Request.makeCache({
  capacity: 1000,
  timeToLive: "30 minutes"
})

const program = getUserById(1).pipe(
  Effect.withRequestCache(customCache)
)

Disabling Batching

const noBatching = program.pipe(
  Effect.withRequestBatching(false)
)

Complete Example

import { Effect, Request, RequestResolver, Schema } from "effect"

// Error types
class UserNotFound extends Schema.TaggedError<UserNotFound>()(
  "UserNotFound",
  { id: Schema.Number }
) {}

// Request type
interface GetUserById extends Request.Request<User, UserNotFound> {
  readonly _tag: "GetUserById"
  readonly id: number
}
const GetUserById = Request.tagged<GetUserById>("GetUserById")

// Batched resolver
const UserResolver = RequestResolver.makeBatched(
  (requests: ReadonlyArray<GetUserById>) =>
    Effect.gen(function* () {
      const ids = requests.map((r) => r.id)

      const response = yield* Effect.tryPromise({
        try: () =>
          fetch("/api/users/batch", {
            method: "POST",
            headers: { "Content-Type": "application/json" },
            body: JSON.stringify({ ids })
          }).then((r) => r.json() as Promise<User[]>),
        catch: () => new Error("Batch fetch failed")
      })

      yield* Effect.forEach(requests, (request, index) => {
        const user = response[index]
        return user
          ? Request.completeEffect(request, Effect.succeed(user))
          : Request.completeEffect(
              request,
              Effect.fail(new UserNotFound({ id: request.id }))
            )
      })
    })
)

// Query function
const getUserById = (id: number) =>
  Effect.request(GetUserById({ id }), UserResolver)

// Usage - automatically batched
const program = Effect.gen(function* () {
  const todos = yield* getTodos()

  const owners = yield* Effect.forEach(
    todos,
    (todo) => getUserById(todo.ownerId),
    { concurrency: "unbounded" }
  )

  return owners
})

Best Practices

  1. Use batching for N+1 scenarios - Especially with databases/APIs
  2. Cache expensive computations - Use Effect.cached
  3. Set appropriate TTLs - Balance freshness vs performance
  4. Use Request deduplication - Automatic with Effect.request
  5. Batch at API boundaries - Group related requests

Additional Resources

For comprehensive batching and caching documentation, consult ${CLAUDE_PLUGIN_ROOT}/references/llms-full.txt.

Search for these sections:

  • "Batching" for complete batching guide
  • "Caching" for caching patterns
  • "Cache" for Cache service
  • "Caching Effects" for Effect.cached patterns

平台分布

Claude Code

27.6%
按下载量换算2,501

windsurf

25.07%
按下载量换算2,272

OpenCode

17.45%
按下载量换算1,581

Codex

12.99%
按下载量换算1,177

Antigravity

7.68%
按下载量换算696

Gemini CLI

3.67%
按下载量换算333

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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