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研究检索敏感数据github未标认证来源可访问许可证需确认审计提醒

research-online在线研究

Agent Skill

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。

总安装

285

周安装

12

GitHub Stars

公开资料未说明

下载量

379
CodexClaudeCursorGemini CLI

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: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

简介

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。

SKILL.md

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 The indexed version could be outdated — check which version is documented before citing it as authoritative.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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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

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

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来源信息

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