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研究检索external-servicegithub未标认证来源可访问clear审计提醒

research研究

Agent Skill

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。

总安装

360

周安装

15

GitHub Stars

26

下载量

120
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

3

许可证

MIT

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(研究)
来源仓库:https://github.com/outfitter-dev/agents
仓库路径:skills/research
安装命令:
npx skills add https://github.com/outfitter-dev/agents --skill 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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/outfitter-dev/agents --skill research

简介

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。

  • 适用于通用研究支持、文献检索或信息聚合等场景。
  • 通过关键词匹配返回相关论文、报告或开源项目。
  • 安装命令为 npx skills add https://github.com/outfitter-dev/agents --skill research。
  • 使用前请确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。

SKILL.md

Research

Systematic investigation → evidence-based analysis → authoritative recommendations.

Steps

  1. Define scope and evaluation criteria
  2. Discover sources using MCP tools (context7, octocode, firecrawl)
  3. Gather information with multi-source approach
  4. Load the outfitter:report-findings skill for synthesis
  5. Compile report with confidence levels and citations

<when_to_use>

  • Technology evaluation and comparison
  • Documentation discovery and troubleshooting
  • Best practices and industry standards research
  • Implementation guidance with authoritative sources

NOT for: quick lookups, well-known patterns, time-critical debugging without investigation stage

</when_to_use>

Load the maintain-tasks skill for stage tracking. Stages advance only, never regress.

StageTriggeractiveForm
Analyze RequestSession start"Analyzing research request"
Discover SourcesCriteria defined"Discovering sources"
Gather InformationSources identified"Gathering information"
Synthesize FindingsInformation gathered"Synthesizing findings"
Compile ReportSynthesis complete"Compiling report"

Workflow:

  • Start: Create "Analyze Request" as in_progress
  • Transition: Mark current completed, add next in_progress
  • Simple queries: Skip directly to "Gather Information" if unambiguous
  • Gaps during synthesis: Add new "Gather Information" task
  • Early termination: Skip to "Compile Report" with caveats

Five-stage systematic approach:

1. Question Stage — Define scope

  • Decision to be made?
  • Evaluation parameters? (performance, maintainability, security, adoption)
  • Constraints? (timeline, expertise, infrastructure)

2. Discovery Stage — Multi-source retrieval

Use CasePrimarySecondaryTertiary
Official docscontext7octocodefirecrawl
Troubleshootingoctocode issuesfirecrawl communitycontext7 guides
Code examplesoctocode reposfirecrawl tutorialscontext7 examples
Technology evalParallel allCross-referenceValidate

3. Evaluation Stage — Analyze against criteria

CriterionMetrics
PerformanceBenchmarks, latency, throughput, memory
MaintainabilityCode complexity, docs quality, community activity
SecurityCVEs, audits, compliance
AdoptionDownloads, production usage, industry patterns

4. Comparison Stage — Systematic tradeoff analysis

For each option: Strengths → Weaknesses → Best fit → Deal breakers

5. Recommendation Stage — Clear guidance with rationale

Primary recommendation → Alternatives → Implementation steps → Limitations

Three MCP servers for multi-source research:

ToolBest ForKey Functions
context7Official docs, API refsresolve-library-id, get-library-docs
octocodeCode examples, issuespackageSearch, githubSearchCode, githubSearchIssues
firecrawlTutorials, benchmarkssearch, scrape, map

Execution patterns:

  • Parallel: Run independent queries simultaneously for speed
  • Fallback: context7 → octocode → firecrawl if primary fails
  • Progressive: Start broad, narrow based on findings

See tool-selection.md for detailed usage.

<discovery_patterns>

Common research workflows:

ScenarioApproach
Library InstallationPackage search → Official docs → Installation guide
Error ResolutionParse error → Search issues → Official troubleshooting → Community solutions
API ExplorationDocumentation ID → API reference → Real usage examples
Technology ComparisonParallel all sources → Cross-reference → Build matrix → Recommend

See discovery-patterns.md for detailed workflows.

</discovery_patterns>

<findings_format>

Two output modes:

Evaluation Mode (recommendations):

Finding: { assertion }
Source: { authoritative source with link }
Confidence: High/Medium/Low — { rationale }

Discovery Mode (gathering):

Found: { what was discovered }
Source: { where from with link }
Notes: { context or caveats }

</findings_format>

<response_structure>

## Research Summary
Brief overview — what investigated, sources consulted.

## Options Discovered
1. **Option A** — description
2. **Option B** — description

## Comparison Matrix
| Criterion | Option A | Option B |
|-----------|----------|----------|

## Recommendation
### Primary: [Option Name]
**Rationale**: reasoning + evidence
**Confidence**: level + explanation

### Alternatives
When to choose differently.

## Implementation Guidance
Next steps, common pitfalls, validation.

## Sources
- Official, benchmarks, case studies, community

</response_structure>

Always include:

  • Direct citations with links
  • Confidence levels and limitations
  • Context about when recommendations may not apply

Always validate:

  • Version is latest stable
  • Documentation matches user context
  • Critical info cross-referenced
  • Code examples complete and runnable

Proactively flag:

  • Deprecated approaches with modern alternatives
  • Missing prerequisites
  • Common pitfalls and gotchas
  • Related tools in ecosystem

ALWAYS:

  • Create "Analyze Request" todo at session start
  • One stage in_progress at a time
  • Use multi-source approach (context7, octocode, firecrawl)
  • Provide direct citations with links
  • Cross-reference critical information
  • Include confidence levels and limitations

NEVER:

  • Skip "Analyze Request" stage without defining scope
  • Single-source when multi-source available
  • Deliver recommendations without citations
  • Include deprecated approaches without flagging
  • Omit limitations and edge cases

Research vs Report-Findings:

  • This skill (research) covers the full investigation workflow using MCP tools
  • report-findings skill covers synthesis, source assessment, and presentation

Use research for technology evaluation, documentation discovery, and best practices research. Load report-findings during synthesis stage for source authority assessment and confidence calibration.

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

04

需要参考平台分布和安装热度时

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

OpenCode

27.38%
按下载量换算33

github-copilot

26.14%
按下载量换算31

Claude Code

16.36%
按下载量换算20

kilo

12.95%
按下载量换算16

windsurf

8.59%
按下载量换算10

zencoder

3.39%
按下载量换算4

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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 warning/failed 结果,不能写成本站确认安全。

来源信息

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