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Agent Skill · 研究检索

parallel-research(平行研究)

parallel-research 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-research 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 73,675 个 Skill 里排第 22,055。

总安装 4,668周安装 187GitHub Stars 11下载 1,511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-research(平行研究)
来源仓库:https://github.com/casper-studios/casper-marketplace
仓库路径:skills/parallel-research
安装命令:
npx skills add https://github.com/casper-studios/casper-marketplace --skill parallel-research
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/casper-studios/casper-marketplace --skill parallel-research

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-research 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。适用于多主题并行调研、跨领域知识整合和复杂问题拆解等研究场景。

通过 GitHub 仓库安装,使用 npx skills add 命令添加指定技能。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。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。

来源仓库内路径:skills/parallel-research

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/casper-studios/casper-marketplace --skill para…;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-research 怎么安装?

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

parallel-research 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-research 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-research 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-research 用的人多吗?

公开数据是累计安装 4,668 次,近一周安装 187 次,下载 1,511 次,GitHub 11 星。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-research 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-research

平行研究 · 本页

4,66811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 Research

Overview

Deep web research, competitive intelligence, entity discovery, and data enrichment using Parallel AI's specialized APIs.

Quick Decision Tree

What do you need?
│
├── Quick factual answer (3-5 seconds)
│   └── Chat API ($0.005/request)
│   └── Script: scripts/parallel_research.py chat "question"
│
├── Comprehensive research report (5min-2hr)
│   └── Deep Research API ($0.30/report for ultra)
│   └── Script: scripts/parallel_research.py research "topic"
│
├── Find entities matching criteria (companies, people)
│   └── FindAll API ($0.03 + $0.10/match)
│   └── Script: scripts/parallel_research.py findall "query"
│
└── Enrich existing data (add fields to records)
    └── Task API with schema ($0.025/record for core)
    └── Script: scripts/parallel_research.py enrich data.csv

Environment Setup

# Required in .env
PARALLEL_API_KEY=your_api_key_here

Get your API key: https://platform.parallel.ai/settings/api-keys

Common Usage

Quick Q&A

python scripts/parallel_research.py chat "What is Anthropic's latest funding round?"

Deep Research Report

python scripts/parallel_research.py research "Competitive landscape of AI code editors in 2025" --processor ultra

Find Companies

python scripts/parallel_research.py findall "AI code editor companies that raised funding in 2024-2025" --limit 50

Basic Research (Simplified)

python scripts/basic_research.py "Company Name"

Vendor Selection

python scripts/vendor_selection.py "CRM software" --requirements "enterprise,API,automation"

Processor Tiers

ProcessorCost/1KLatencyBest For
lite$510-60sBasic metadata
base$1015-100sSimple research
core$251-5minCross-referenced research
pro$1002-10minExploratory research
ultra$3005-25minDeep research (recommended)
ultra-fast$3002-10minSpeed + quality

Cost Estimates

TaskAPICost
100 quick questionsChat$0.50
Market research reportDeep Research (ultra)$0.30
Find 50 competitorsFindAll (core)~$5.00
Enrich 100 leadsTask (core)$2.50

Free Tier

20,000 requests free (combined across all APIs).

Security Notes

Credential Handling

Data Privacy

  • Research queries are sent to Parallel AI servers
  • Research outputs may contain third-party company information
  • Results are stored locally in .tmp/ directory
  • Parallel AI may log queries for service improvement
  • Avoid including sensitive internal data in research queries

Access Scopes

  • API key provides full access to all research endpoints
  • No granular permission scopes available
  • Monitor usage and costs via Parallel AI dashboard

Compliance Considerations

  • Data Sources: Research pulls from public web sources
  • Citation: Always cite sources in research outputs
  • Accuracy: AI-generated research should be verified
  • Competitive Intel: Ensure competitive research complies with policies
  • Third-Party Data: Respect intellectual property of sources
  • PII in Results: Research results may contain company/individual PII
  • Data Freshness: Verify currency of time-sensitive information

Troubleshooting

Common Issues

Issue: Processor timeout

Symptoms: Request times out or returns partial results Cause: Complex query requiring more processing time than allowed Solution:

  • Use a faster processor tier (lite or base instead of ultra)
  • Simplify the research query
  • Break complex queries into multiple smaller requests
  • Increase timeout in script if configurable
Issue: Credits exhausted

Symptoms: "Insufficient credits" or quota error Cause: Account credits depleted Solution:

  • Check balance at https://platform.parallel.ai/dashboard
  • Upgrade plan or purchase additional credits
  • Use lower-cost processor tiers for less critical queries
  • Monitor usage to avoid unexpected depletion
Issue: Invalid response format

Symptoms: JSON parsing error or unexpected response structure Cause: API returned error or malformed response Solution:

  • Check query format matches API requirements
  • Retry the request (may be transient issue)
  • Verify API key is valid and active
  • Review API documentation for expected response format
Issue: Empty or irrelevant results

Symptoms: Research returns no results or off-topic content Cause: Query too narrow, ambiguous, or poorly structured Solution:

  • Broaden the search query
  • Add context to clarify query intent
  • Try different phrasing or keywords
  • Use Chat API first to validate query understanding
Issue: API authentication failed

Symptoms: "Invalid API key" or 401 error Cause: API key expired, invalid, or not set Solution:

Issue: Rate limited

Symptoms: 429 error or "rate limit exceeded" Cause: Too many concurrent requests Solution:

  • Add delays between requests
  • Reduce parallel request count
  • Implement exponential backoff
  • Contact support for higher rate limits if needed

Resources

  • references/api-guide.md - Complete API documentation
  • references/basic-research.md - Simple company research
  • references/vendor-selection.md - Vendor comparison workflow

Integration Patterns

Research to Report

Skills: parallel-research → content-generation Use case: Create polished reports from research findings Flow:

  1. Run deep research on topic/company
  2. Generate structured research output
  3. Format into branded document via content-generation

FindAll to CRM

Skills: parallel-research → attio-crm Use case: Populate CRM with discovered companies Flow:

  1. Use FindAll to discover companies matching criteria
  2. Enrich each company with additional data
  3. Create/update company records in Attio CRM

Research to Sheets

Skills: parallel-research → google-workspace Use case: Build research database in Google Sheets Flow:

  1. Run FindAll or batch research on multiple entities
  2. Structure results as tabular data
  3. Upload to Google Sheets for team collaboration

平台分布

Claude

32.41%
按下载量换算490

Codex

31.47%
按下载量换算476

Cursor

18.58%
按下载量换算281

Gemini CLI

9.88%
按下载量换算149

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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