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Agent Skill · 研究检索

parallel-ai-search(并行 AI 搜索)

parallel-ai-search 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。

parallel-ai-search 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,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,674 个 Skill 里排第 41,143。

总安装 1,212周安装 51GitHub Stars 公开资料未说明下载 424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
unknown

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:parallel-ai-search(并行 AI 搜索)
来源仓库:https://github.com/tristanmanchester/agent-skills
仓库路径:skills/parallel-ai-search
安装命令:
npx skills add https://github.com/tristanmanchester/agent-skills --skill parallel-ai-search
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/tristanmanchester/agent-skills --skill parallel-ai-search

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。

parallel-ai-search 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。适用于并行搜索、多源信息聚合或 AI 增强检索等场景。通过关键词匹配和来源仓库内容,支持并发检索多个数据源并整合结果。

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。可结合来源仓库、安装命令和原始 README 继续核验具体用法和功能边界。

来源仓库内路径:skills/parallel-ai-search

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 https://github.com/tristanmanchester/agent-skills --skill paralle…;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

    它需要接触敏感数据,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

parallel-ai-search 怎么安装?

在终端执行 npx skills add https://github.com/tristanmanchester/agent-skills --skill parallel-ai-searc…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

parallel-ai-search 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。

parallel-ai-search 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

parallel-ai-search 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

parallel-ai-search 用的人多吗?

公开数据是累计安装 1,212 次,近一周安装 51 次,下载 424 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。

parallel-ai-search 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
parallel-ai-search

并行 AI 搜索 · 本页

1,212—接触敏感数据未明确有提醒

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

Parallel AI Search (CLI Master)

This is a single “master” skill that replaces the earlier Node-script-based version of parallel-ai-search.

It routes to the right parallel-cli capability for the task:

  • Search: quick web lookup with citations (parallel-cli search)
  • Extract: turn URLs (including PDFs and JS-heavy pages) into clean, LLM-ready text (parallel-cli extract)
  • Deep research: multi-source reports with processor tiers (parallel-cli research...)
  • Enrich: add web-sourced columns to CSV/JSON (parallel-cli enrich...)
  • FindAll: discover entities from the web with optional enrichments (parallel-cli findall...)
  • Monitor: track web changes on a cadence, optionally via webhook (parallel-cli monitor...)

Routing rules (pick ONE)

Choose the smallest / cheapest action that solves the user’s request:

  1. Extract — if the user gives one or more URLs *or* says “read/summarise this page”, “extract”, “quote”, “pull the content”, “what does this page say”.
  2. Deep research — ONLY if the user explicitly asks for *deep*, *exhaustive*, *comprehensive*, *thorough investigation*, or a multi-source “report”.
  3. Enrich — if the user provides a list/table (CSV/JSON/inline objects) and wants new columns like CEO, revenue, funding, contact info, etc.
  4. FindAll — if the user wants you to discover many entities (companies/people/venues/etc.) that match criteria.
  5. Monitor — if the user wants ongoing tracking (“alert me”, “track changes”, “monitor this weekly”) rather than a one-off answer.
  6. Search — default for everything else that needs current web info or citations.

Optional manual prefixes if the user invoked this skill directly:

  • search:...
  • extract:...
  • research:...
  • enrich:...
  • findall:...
  • monitor:...

If a prefix is present, honour it.

Setup and authentication (only when needed)

Before running any Parallel command, ensure auth works:

parallel-cli auth

If parallel-cli is missing, install it:

curl -fsSL https://parallel.ai/install.sh | bash

If you cannot use the install script, use pipx:

pipx install "parallel-web-tools[cli]"
pipx ensurepath

Then authenticate (choose one):

# Interactive OAuth (opens browser)
parallel-cli login

# Headless / SSH / CI
parallel-cli login --device

# Or environment variable
export PARALLEL_API_KEY="your_api_key"

Output & citation rules

  • Always cite web-sourced facts with inline markdown links: [Source Title](https://...).
  • End with a Sources list whenever you used Search/Extract/Research output.
  • Prefer official/primary sources when available.
  • For long outputs, save to files in /tmp/ and summarise in-chat.

Search (default web lookup)

Use Search for fast, cost-effective answers with citations.

Command template

parallel-cli search "$OBJECTIVE"   --mode agentic   --max-results 10   --json

Add any of these only when relevant:

  • --after-date YYYY-MM-DD (freshness constraint)
  • --include-domains a.com b.org (restrict sources)
  • --exclude-domains spam.com (block sources)
  • one or more -q "keyword query" flags (extra keyword probes)
  • -o "/tmp/$SLUG.search.json" (save full JSON to a file)

Parse + respond

From the JSON results, extract title, url, and any publish_date / excerpt fields. Answer the user’s question, and cite each claim inline.

Extract (read one or more URLs)

Use Extract when you need the actual contents of specific URLs (webpages, PDFs, JS-heavy sites).

Command template

parallel-cli extract "$URL" --json

Add when relevant:

  • --objective "Focus area" (e.g., pricing, API usage, constraints)
  • --full-content (only if the user needs the whole page)
  • --no-excerpts (if you only want full content)
  • -o "/tmp/$SLUG.extract.json" (save full JSON to a file)

Respond

  • If the user asked for a summary, summarise with citations to the extracted URL.
  • If the user asked for the verbatim text, provide the extracted markdown *only if it is reasonably sized*; otherwise provide the key sections + offer to read more from the saved output.

Deep research (only when explicitly requested)

Deep research is slower and may cost more than Search. Use it only when the user explicitly wants depth.

Step 1 — start (always async)

parallel-cli research run "$QUESTION" --processor pro-fast --no-wait --json

Parse run_id (and any monitoring URL) from JSON and tell the user the run started.

Step 2 — poll (bounded timeout)

Choose a short slug filename (lowercase-hyphen), then:

parallel-cli research poll "$RUN_ID" -o "/tmp/$SLUG" --timeout 540
  • Share the executive summary printed by the poll command.
  • Mention the output files:

- /tmp/$SLUG.md - /tmp/$SLUG.json

If polling times out, re-run the same poll command — the run continues server-side.

Enrich (CSV/JSON or inline data)

Use Enrich to add web-sourced columns to structured data.

Step 1 — (optional) suggest columns

parallel-cli enrich suggest "$INTENT" --json

Use this when the user knows the goal but not the exact output schema.

Step 2 — run (always async for large jobs)

For CSV:

parallel-cli enrich run   --source-type csv   --source "input.csv"   --target "/tmp/enriched.csv"   --source-columns '[{"name":"company","description":"Company name"}]'   --intent "$INTENT"   --no-wait --json

For inline JSON rows:

parallel-cli enrich run   --data '[{"company":"Google"},{"company":"Apple"}]'   --target "/tmp/enriched.csv"   --intent "$INTENT"   --no-wait --json

Parse taskgroup_id from JSON.

Step 3 — poll

parallel-cli enrich poll "$TASKGROUP_ID" --timeout 540 --json

After completion:

  • Tell the user the output file path (the --target you chose).
  • Preview a few rows (using file read tools if available) and report row counts.

If poll times out, re-run it — the job continues server-side.

FindAll (entity discovery)

Use FindAll when the user wants you to discover a set of entities (e.g., “AI startups in healthcare”, “roofing companies in Charlotte”, “YC devtools companies”).

Step 1 — run

parallel-cli findall run "$OBJECTIVE" --generator core --match-limit 25 --no-wait --json

Useful options:

  • --dry-run --json to preview schema before spending money
  • --exclude '[{"name":"Example Corp","url":"example.com"}]' to avoid known entities
  • --generator preview|base|core|pro (core default; pro for hardest queries)

Parse run_id from JSON.

Step 2 — poll + fetch results

parallel-cli findall poll "$RUN_ID" --json
parallel-cli findall result "$RUN_ID" --json

Respond with:

  • total entities found
  • a clean list/table of the best matches (name + URL + key attributes)
  • any caveats about ambiguous matches

Monitor (web change tracking)

Use Monitor when the user wants ongoing tracking.

Create:

parallel-cli monitor create "$OBJECTIVE" --cadence daily --json

Optional:

  • --cadence hourly|daily|weekly|every_two_weeks
  • --webhook https://example.com/hook (deliver events externally)
  • --output-schema '<JSON schema string>' (structured events)

Manage:

parallel-cli monitor list --json
parallel-cli monitor get "$MONITOR_ID" --json
parallel-cli monitor update "$MONITOR_ID" --cadence weekly --json
parallel-cli monitor delete "$MONITOR_ID"
parallel-cli monitor events "$MONITOR_ID" --json
parallel-cli monitor simulate "$MONITOR_ID" --json

Respond with the monitor id and how to retrieve events (or confirm webhook delivery).

Reference material

  • Copy/paste command templates and patterns: references/command-templates.md
  • Troubleshooting common failures: references/troubleshooting.md

平台分布

Codex

33.11%
按下载量换算140

Claude

32.95%
按下载量换算140

Cursor

17.52%
按下载量换算74

Gemini CLI

9.83%
按下载量换算42

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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