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research-and-report研究与报告

Agent Skill

research-and-report 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

605

周安装

26

GitHub Stars

公开资料未说明

下载量

212
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

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-and-report(研究与报告)
来源仓库:https://github.com/outfitter-dev/agents
仓库路径:skills/research-and-report
安装命令:
npx skills add outfitter-dev/agents --skill "research-and-report"
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。

AgentSkills.tonpx skills
npx skills add outfitter-dev/agents --skill "research-and-report"

简介

发现并安装 AI 代理的技能,用于扩展 Agent 能力。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境。
  • 通过 GitHub 仓库安装,支持技能动态加载。
  • 需确认 token 权限及是否允许联网或外部调用。
  • 建议检查仓库维护状态和技能兼容性。research-and-report 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

Systematic investigation → evidence-based analysis → authoritative recommendations.

<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 phase

</when_to_use>

Track with TodoWrite. Phases advance only, never regress.

PhaseTriggeractiveForm
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-phase systematic approach:

1. Question Phase — Define scope

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

2. Discovery Phase — Multi-source retrieval

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

3. Evaluation Phase — Analyze against criteria

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

4. Comparison Phase — Systematic tradeoff analysis

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

5. Recommendation Phase — 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 phase 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" phase without defining scope
  • Single-source when multi-source available
  • Deliver recommendations without citations
  • Include deprecated approaches without flagging
  • Omit limitations and edge cases

Deep-dive documentation:

Related resources:

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

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

Claude Code

30.63%
按下载量换算65

windsurf

22.1%
按下载量换算47

OpenCode

16.17%
按下载量换算34

Cursor

13.26%
按下载量换算28

Codex

8.43%
按下载量换算18

Antigravity

3.31%
按下载量换算7

安全审计

暂无安全审计结果可展示。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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