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parallel-deep-research并行深度研究

Agent Skill

parallel-deep-research 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

总安装

126,326

周安装

5,371

GitHub Stars

3

下载量

44,257
OpenClaw

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

MIT-0

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:parallel-deep-research(并行深度研究)
来源仓库:https://github.com/normallygaussian/parallel-deep-research
安装命令:
openclaw skills install parallel-deep-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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install parallel-deep-research

简介

通过并行 API 进行深入的多源研究。当用户明确要求对某个主题进行彻底研究、全面分析或调查时使用。要快速查找或新闻,请改用并行搜索。

SKILL.md

name
parallel-deep-research
description
Deep multi-source research via Parallel API. Use when user explicitly asks for thorough research, comprehensive analysis, or investigation of a topic. For quick lookups or news, use parallel-search instead.
homepage
https://parallel.ai

Parallel Deep Research

Deep, multi-source research for complex topics requiring synthesis from many sources. Returns comprehensive reports with citations.

When to Use

Trigger this skill when the user asks for:

  • "deep research on...", "thorough investigation of...", "comprehensive report about..."
  • "research everything about...", "full analysis of..."
  • Complex topics requiring synthesis from 10+ sources
  • Competitive analysis, market research, due diligence
  • Questions where depth and accuracy matter more than speed

NOT for:

  • Quick lookups or simple questions (use parallel-search)
  • Current news or recent events (use parallel-search with --after-date)
  • Reading specific URLs (use parallel-extract)

Quick Start

parallel-cli research run "your research question" --processor pro-fast --json -o ./report

CLI Reference

Basic Usage

parallel-cli research run "<question>" [options]

Common Flags

FlagDescription
-p, --processor <tier>Processor tier (see table below)
--jsonOutput as JSON
-o, --output <path>Save results to file (creates .json and .md)
-f, --input-file <path>Read query from file (for long questions)
--timeout NMax wait time in seconds (default: 3600)
--no-waitReturn immediately, poll later with research status

Processor Tiers

ProcessorTimeUse Case
lite-fast10-20sQuick lookups
base-fast15-50sSimple questions
core-fast15s-100sModerate research
pro-fast30s-5minExploratory research (default)
ultra-fast1-10minMulti-source deep research
ultra2x-fast1-20minDifficult deep research
ultra4x-fast1-40minVery difficult research
ultra8x-fast1min-1hrMost challenging research

Non-fast variants (e.g., pro, ultra) take longer but use fresher data.

Examples

Basic research:

parallel-cli research run "What are the latest developments in quantum computing?" \
  --processor pro-fast \
  --json -o ./quantum-report

Deep competitive analysis:

parallel-cli research run "Compare Stripe, Square, and Adyen payment platforms: features, pricing, market position, and developer experience" \
  --processor ultra-fast \
  --json -o ./payments-analysis

Long research question from file:

# Create question file
cat > /tmp/research-question.txt << 'EOF'
Investigate the current state of AI regulation globally:
1. What regulations exist in the US, EU, and China?
2. What's pending or proposed?
3. How do companies like OpenAI, Google, and Anthropic respond?
4. What industry groups are lobbying for/against regulation?
EOF

parallel-cli research run -f /tmp/research-question.txt \
  --processor ultra-fast \
  --json -o ./ai-regulation-report

Non-blocking research:

# Start research without waiting
parallel-cli research run "research question" --no-wait

# Check status later
parallel-cli research status <task-id>

# Poll until complete
parallel-cli research poll <task-id> --json -o ./report

Best-Practice Prompting

Research Question

Write 2-5 sentences describing:

  • The specific question or topic
  • Scope boundaries (time period, geography, industries)
  • What aspects matter most (pricing? features? market share?)
  • Desired output format (comparison table, timeline, pros/cons)

Good:

Compare the top 5 CRM platforms for B2B SaaS companies with 50-200 employees.
Focus on: pricing per seat, integration ecosystem, reporting capabilities.
Include recent 2024-2026 changes and customer reviews from G2/Capterra.

Poor:

Tell me about CRMs

Response Format

Returns structured JSON with:

  • task_id — unique identifier for polling
  • statuspending, running, completed, failed
  • result — when complete:

- summary — executive summary - findings[] — detailed findings with sources - sources[] — all referenced URLs with titles

Output Handling

When presenting research results:

  • Lead with the executive summary verbatim
  • Present key findings without paraphrasing
  • Include source URLs for all facts
  • Note any conflicting information between sources
  • Preserve all facts, names, numbers, dates, quotes

Running Out of Context?

For long conversations, save results and use sessions_spawn:

parallel-cli research run "<question>" --json -o /tmp/research-<topic>

Then spawn a sub-agent:

{
  "tool": "sessions_spawn",
  "task": "Read /tmp/research-<topic>.json and present the executive summary and key findings with sources.",
  "label": "research-summary"
}

Error Handling

Exit CodeMeaning
0Success
1Unexpected error (network, parse)
2Invalid arguments
3API error (non-2xx)

Prerequisites

  1. Get an API key at parallel.ai
  2. Install the CLI:
curl -fsSL https://parallel.ai/install.sh | bash
export PARALLEL_API_KEY=your-key

References

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

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安全审计

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可疑

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权限和风险

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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