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exa-data-handlingex 数据处理

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

599

周安装

24

GitHub Stars

2,077

下载量

194
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:exa-data-handling(ex 数据处理)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/exa-data-handling
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill exa-data-handling
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。

skills.shnpx skills
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill exa-data-handling

简介

exa-data-handling 管理 Exa 搜索结果数据,包括内容过滤、缓存与去重处理。

  • 适用于大规模内容处理、RAG 管道构建或搜索结果二次加工的场景。
  • 支持按元数据筛选、TTL 缓存与引用去重等高级数据处理能力。
  • 使用前应确认存储层可用性,避免因缓存失效导致性能下降。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Exa Data Handling

Overview

Manage search result data from Exa neural search APIs. Covers content extraction filtering, result caching with TTL, citation deduplication, and handling large content payloads efficiently for RAG pipelines.

Prerequisites

  • Exa API key
  • exa-js SDK installed
  • Storage layer for cached results
  • Understanding of content extraction options

Instructions

Step 1: Control Content Extraction Scope

import Exa from 'exa-js';

const exa = new Exa(process.env.EXA_API_KEY!);

// Minimal extraction: metadata only (cheapest)
async function searchMetadataOnly(query: string) {
  return exa.search(query, {
    numResults: 10,
    type: 'auto',
    // No contents - just URLs, titles, scores
  });
}

// Controlled extraction: highlights only (balanced)
async function searchWithHighlights(query: string) {
  return exa.searchAndContents(query, {
    numResults: 10,
    highlights: { numSentences: 3, highlightsPerUrl: 2 },
    // No full text - reduces payload significantly
  });
}

// Full extraction: text with character limit
async function searchWithText(query: string, maxChars = 2000) {  # 2000: 2 seconds in ms
  return exa.searchAndContents(query, {
    numResults: 5,
    text: { maxCharacters: maxChars },
    highlights: { numSentences: 3 },
  });
}

Step 2: Result Caching with TTL

import { LRUCache } from 'lru-cache';
import { createHash } from 'crypto';

const searchCache = new LRUCache<string, any>({
  max: 500,  # HTTP 500 Internal Server Error
  ttl: 1000 * 60 * 60, // 1 hour default  # 1000: 1 second in ms
});

function cacheKey(query: string, options: any): string {
  return createHash('sha256')
    .update(JSON.stringify({ query, ...options }))
    .digest('hex');
}

async function cachedSearch(
  query: string,
  options: any = {},
  ttlMs?: number
) {
  const key = cacheKey(query, options);
  const cached = searchCache.get(key);
  if (cached) return cached;

  const results = await exa.searchAndContents(query, options);
  searchCache.set(key, results, { ttl: ttlMs });
  return results;
}

Step 3: Content Size Management

interface ProcessedResult {
  url: string;
  title: string;
  score: number;
  snippet: string;  // Truncated content
  contentSize: number;
}

function processResults(results: any[], maxSnippetLength = 500): ProcessedResult[] {  # HTTP 500 Internal Server Error
  return results.map(r => ({
    url: r.url,
    title: r.title || 'Untitled',
    score: r.score,
    snippet: (r.text || r.highlights?.join(' ') || '').slice(0, maxSnippetLength),
    contentSize: (r.text || '').length,
  }));
}

// Estimate token count for LLM context budgets
function estimateTokens(results: ProcessedResult[]): number {
  const totalChars = results.reduce((sum, r) => sum + r.snippet.length, 0);
  return Math.ceil(totalChars / 4); // Rough estimate: 4 chars per token
}

function fitToTokenBudget(results: ProcessedResult[], maxTokens: number) {
  const sorted = results.sort((a, b) => b.score - a.score);
  const selected: ProcessedResult[] = [];
  let tokenCount = 0;

  for (const result of sorted) {
    const resultTokens = Math.ceil(result.snippet.length / 4);
    if (tokenCount + resultTokens > maxTokens) break;
    selected.push(result);
    tokenCount += resultTokens;
  }

  return { selected, tokenCount };
}

Step 4: Citation Deduplication

function deduplicateCitations(results: any[]): any[] {
  const seen = new Map<string, any>();

  for (const result of results) {
    const domain = new URL(result.url).hostname;
    const key = `${domain}:${result.title}`;

    if (!seen.has(key) || result.score > seen.get(key).score) {
      seen.set(key, result);
    }
  }

  return Array.from(seen.values());
}

Error Handling

IssueCauseSolution
Large response payloadRequesting full text for many URLsUse highlights or limit maxCharacters
Cache stale for newsDefault TTL too longUse shorter TTL for time-sensitive queries
Duplicate sourcesSame article from multiple domainsDeduplicate by domain + title
Token budget exceededToo much context for LLMUse fitToTokenBudget to trim

Examples

RAG-Optimized Search

async function ragSearch(query: string, tokenBudget = 3000) {  # 3000: 3 seconds in ms
  const results = await cachedSearch(query, {
    numResults: 15,
    text: { maxCharacters: 1500 },  # 1500 = configured value
    highlights: { numSentences: 3 },
  });

  const processed = processResults(results.results);
  const { selected } = fitToTokenBudget(processed, tokenBudget);
  return selected;
}

Resources

Output

  • Configuration files or code changes applied to the project
  • Validation report confirming correct implementation
  • Summary of changes made and their rationale

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能力 4

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.6%
按下载量换算65

Claude

31.33%
按下载量换算61

Cursor

17.68%
按下载量换算34

Gemini CLI

9.15%
按下载量换算18

安全审计

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敏感数据

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安装前确认

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来源信息

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