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Agent Skill · 研究检索

research-online(在线研究)

research-online 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。

research-online 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 65,208。

总安装 285周安装 12GitHub Stars 公开资料未说明下载 379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:research-online(在线研究)
来源仓库:https://github.com/nielsmadan/agentic-coding
仓库路径:skills/research-online
安装命令:
npx skills add https://github.com/nielsmadan/agentic-coding --skill research-online
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/nielsmadan/agentic-coding --skill research-online

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。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于需要根据关键词或任务场景进行信息检索的研究类 Agent 工作流。通过 npx skills add 命令从指定 GitHub 仓库安装使用。

建议确认权限范围和维护状态,注意可能触发联网或文件读写操作。research-online 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/research-online

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/nielsmadan/agentic-coding --skill research-onl…;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

research-online 怎么安装?

在终端执行 npx skills add https://github.com/nielsmadan/agentic-coding --skill research-online。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

research-online 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。

research-online 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。

research-online 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。

research-online 用的人多吗?

公开数据是累计安装 285 次,近一周安装 12 次,下载 379 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。

research-online 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
research-online

在线研究 · 本页

285—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

Research Online

Research a programming topic from multiple angles using parallel sub-agents, with critical evaluation of source credibility.

Usage

/research-online <library> <what you want to do>
/research-online "<error message>" <library>
/research-online how to implement auth in react-navigation v7
/research-online best practices for state management in React
/research-online Redux vs Zustand for large app

Gotchas

  • Context7 docs may lag behind a recent major release. The indexed version could be outdated — check which version is documented before citing it as authoritative.
  • "Prefer recent, then higher authority" can be wrong: an authoritative maintainer comment from 18 months ago may be more correct than a popular blog post from last month. Weigh authority first for stable libraries.
  • Quick mode may miss nuance. If a seemingly simple question turns out to have a complex answer (e.g., "default port" but it depends on framework integration), note this in the synthesis and suggest re-running in Standard mode.

Workflow

Step 1: Parse Input

Extract from the user's query:

  • Library/framework name (e.g., "react-navigation", "prisma", "next.js")
  • Error message (if any, usually in quotes)
  • Version (if mentioned, e.g., "v7", "version 14")
  • Goal/intent (what they're trying to build or achieve)
  • Problem description (if debugging: what's broken or unexpected)
  • Comparison targets (if comparing: "X vs Y", "X or Y")

Step 2: Classify Query Depth

Classify the query to avoid over-researching simple questions:

ModeWhenBehavior
QuickSimple factual lookup, single API question, "what version supports X", "how to do X" with well-known librarySkip internal docs check. Spawn only Docs + General (max 2 agents). Skip follow-up loop and critique. Go straight to synthesis.
StandardComparisons, best practices, errors, complex implementation, "real world experience", debuggingFull workflow including follow-up loop and adversarial critique

When in doubt, use Standard.

Step 3: Check Internal Documentation First (Standard mode only)

Before external research, use the Grep tool to search for relevant keywords in the project's docs:

Grep pattern: "<relevant_keywords>" path: "docs/" and "*.md"

If found, read and include internal patterns/conventions in the synthesis. Internal docs often contain project-specific decisions that external research won't cover.

Step 4: Determine Which Agents to Spawn

AgentSpawn WhenPurpose
DocsLibrary/framework mentionedContext7 documentation lookup
GitHubLibrary with known repositorySearch issues AND discussions
GeneralAlwaysBroad "how to" web search
SpecificError message providedSearch for exact error text
StackOverflowCommon problem/implementation patternCommunity Q&A solutions
ChangelogVersion mentioned OR "stopped working" / "after upgrade"Breaking changes, migration guides
Best PracticesFeature implementation (no error message)Architecture patterns, recommended approaches
RedditComparison, best practices, or "real world experience" queriesCandid developer opinions, warnings, real-world experience
ComparisonQuery contains "vs", "or", "compare", "which", "best library"Compare options, pros/cons

Step 5: Spawn Agents in Parallel

Use the Task tool to spawn ALL relevant agents in a single message (parallel execution). Each agent uses subagent_type: general-purpose.

Every agent must capture metadata for each source: URL, date, and source type (official docs / GitHub issue / blog / SO answer / forum).

AgentToolSearch Strategy
DocsContext7resolve-library-id then query-docs. If resolve-library-id returns no match, fall back to WebSearch for {lib} official documentation {goal}
GitHubWebSearchsite:github.com {lib} "{terms}", then WebFetch top 2-3
GeneralWebSearchhow to {goal} {lib}
SpecificWebSearch"{exact_error_message}" {lib}
StackOverflowWebSearchsite:stackoverflow.com {lib} {keywords}, then WebFetch top answers
ChangelogWebSearch{lib} {version} changelog breaking changes migration
Best PracticesWebSearch{lib} best practices {goal} + {lib} recommended architecture {goal}
RedditWebSearchsite:reddit.com {lib} {keywords}, then WebFetch top 2-3 threads
ComparisonWebSearch{option_A} vs {option_B} {context}

For full agent prompt templates with detailed instructions, see references/agent-prompts.md.

Step 6: Collect and Deduplicate Results

Wait for all agents to complete and gather their findings with metadata. Before evaluation, deduplicate: if multiple agents found the same URL or GitHub issue, keep the entry with the richest metadata and merge any unique context from the duplicates. Note which agents independently found the same source — convergence from multiple agents increases confidence.

Step 7: Critical Evaluation

Before synthesizing, evaluate each source:

Recency (adjust thresholds by library velocity):

AgeFast-moving libs (React, Next.js, etc.)Stable libs (Express, lodash, etc.)
< 6 monthsHighHigh
6-18 monthsMediumHigh
18-36 monthsLowMedium
> 3 yearsVery LowLow

Authority:

Source TypeScore
Official docs, changelogs, core team postsHigh
GitHub issues (maintainer response)High
GitHub issues (community), recent blogs (known author)Medium
SO answers (accepted + >10 votes), comparison articlesMedium
Reddit threads (>50 upvotes or multiple experienced replies)Medium
SO answers (not accepted, <10 votes), old blogs, old comparisonsLow
Reddit threads (<10 upvotes, few replies), random forumsVery Low

Relevance: Exact error/goal match = High. Same library, similar task = Medium. Related concept = Low.

When sources conflict: Prefer more recent, then higher authority. Note conflicts in synthesis. If official docs conflict with recent issues, the issue may reveal a bug or undocumented behavior.

Step 8: Follow-Up Search Loop (Standard mode only)

After critical evaluation, check if any topic area has fewer than 2 sources or if the query's core question remains unanswered. If so:

  1. Identify the gap (e.g., "no sources found for how to handle token refresh specifically")
  2. Generate 1-2 targeted delta queries — more specific terms, alternative terminology, or broader scope
  3. Spawn 1-2 follow-up agents with the delta queries
  4. Merge new results into existing findings, deduplicate, re-evaluate

Max 1 follow-up cycle. If the gap persists, note it in the synthesis as a low-confidence area rather than searching again.

Step 9: Adversarial Critique (Standard mode only)

Brief self-challenge before presenting. Ask:

  • What would someone who disagrees with this conclusion say?
  • Are we over-weighting one source type? (e.g., all findings from blog posts, no official docs)
  • Could any "independent" sources actually trace back to the same original? (e.g., 3 blogs all citing one tweet = 1 source, not 3)
  • Is the recommended approach the simplest option, or are we over-engineering?

If the critique reveals a blind spot, adjust the synthesis and lower the confidence level. This is a brief internal review pass — do not spawn a separate agent.

Step 10: Present Results

Lead with the synthesis, not the raw data. The user wants the answer first, with supporting evidence.

Structure the output as:

  1. Synthesis — goal, recommended approach, confidence level, key findings weighted by credibility. Include the 1-3 most influential references with URLs — the sources that most shaped the conclusion.
  2. Supporting Details — only include sections relevant to the query, and only findings not already covered in the synthesis. Skip sections that would just repeat what's in the synthesis.

Available detail sections (include only those relevant):

  • Documentation — Context7 findings
  • GitHub Issues & Discussions — with date, type, authority
  • Reddit — real-world experience, warnings, opinions with upvote context
  • Comparison — (comparison queries only) pros/cons/best-for per option
  • Specific Error Matches — (error queries only) causes and fixes
  • Version/Changelog — (version queries only) breaking changes
  • Conflicts — only if sources disagree on something material, with resolution

For the full output format template, see references/output-format.md.

Examples

Example 1: Feature Implementation

/research-online how to implement authentication in Next.js 14

Spawns: Docs, GitHub, General, Best Practices, StackOverflow

Example 2: Comparison Query

/research-online Redux vs Zustand for large React app

Spawns: Docs (both), General, Comparison, Reddit, StackOverflow

Example 3: Specific Error

/research-online "Cannot read property 'navigate' of undefined" react-navigation

Spawns: Docs, GitHub, General, Specific, StackOverflow

Example 4: Version-Specific

/research-online auth navigation not working in react-navigation v7

Spawns: Docs, GitHub, General, Changelog, StackOverflow

Example 5: Best Practices

/research-online best practices for folder structure in Express API

Spawns: Docs, General, Best Practices, Reddit, StackOverflow

Example 6: Quick Mode (Simple Lookup)

/research-online what's the default port for Vite dev server

Quick mode: Spawns Docs + General only. Returns direct answer without follow-up loop or critique.

Troubleshooting

Agent fails or times out

Solution: Continue with remaining agents. Note the gap in the synthesis and which source types are missing. The research is still useful with partial results.

No results found for a query

Solution: Widen search terms — try without the library name, use alternative terminology, or search for the underlying concept rather than the specific implementation.

All sources are outdated

Solution: Flag explicitly in the synthesis. Note the dates and recommend the user verify against current documentation. Prefer official docs over old blog posts.

Sources conflict with each other

Solution: Weight by recency and authority. Note the conflict clearly in the Source Evaluation Summary with a resolution explaining which source to trust and why.

Notes

  • All agents run in parallel for speed
  • Each agent should complete in under 60 seconds
  • Always capture source metadata for critical evaluation
  • Weight findings by credibility in synthesis — a recent GitHub issue from a maintainer outweighs a 5-year-old blog post
  • For comparisons, be especially careful about recency — library landscapes change quickly

平台分布

Codex

37.11%
按下载量换算141

Claude

29.49%
按下载量换算112

Cursor

17.36%
按下载量换算66

Gemini CLI

8.32%
按下载量换算32

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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