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Agent Skill · 研究检索

report-writing(报告写作)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

report-writing 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,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 54,591。

总安装 504周安装 21GitHub Stars 48下载 168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:report-writing(报告写作)
来源仓库:https://github.com/hyunjunjeon/deepagent-research-context-engineering
仓库路径:skills/report-writing
安装命令:
npx skills add https://github.com/hyunjunjeon/deepagent-research-context-engineering --skill report-writing
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/hyunjunjeon/deepagent-research-context-engineering --skill report-writing

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。

report-writing 辅助文档、README 和内容稿件的整理与改写,提升可读性和结构清晰度。适用于生成说明文、技术文档或内容稿件的结构化输出。可提炼章节、统一术语、检查链接并整合零散材料为完整文档。使用时保留项目已有事实,避免将未确认信息写成确定结论。涉及对外文案时需控制语气,防止过度营销或夸大能力描述。

来源仓库内路径:skills/report-writing

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/hyunjunjeon/deepagent-research-context-enginee…;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

    它需要需要联网,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

report-writing 怎么安装?

在终端执行 npx skills add https://github.com/hyunjunjeon/deepagent-research-context-engineering --ski…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

report-writing 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。

report-writing 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

report-writing 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。

report-writing 用的人多吗?

公开数据是累计安装 504 次,近一周安装 21 次,下载 168 次,GitHub 48 星。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。

report-writing 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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

  • 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
report-writing

报告写作 · 本页

50448需要联网未明确通过

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

Report Writing Skill

This skill provides structured guidance for transforming research findings into well-organized, professional reports. It ensures consistency, clarity, and completeness across all research outputs.

When to Use This Skill

Invoke this skill when:

  • Finalizing Research: Converting raw research notes and findings into a formal deliverable
  • Creating Documentation: Producing technical documentation, white papers, or analysis reports
  • Synthesizing Multiple Sources: Combining insights from various sub-agent research tasks into a unified narrative
  • Stakeholder Communication: Preparing reports for executive review, technical teams, or external audiences
  • Knowledge Preservation: Documenting research methodology and findings for future reference

Do NOT use this skill for:

  • Quick summaries or informal notes (use simple markdown instead)
  • Real-time status updates (use TODO lists)
  • Raw data dumps (use structured data files)

Report Structure Template

Every research report MUST follow this hierarchical structure. Adapt section depth based on report complexity.

1. Executive Summary

Purpose: Provide a standalone overview for readers who may not read the full report.

Contents:

  • Research objective (1-2 sentences)
  • Key findings (3-5 bullet points)
  • Primary recommendations or conclusions
  • Critical limitations or caveats

Length: 150-300 words (1 page maximum)

Writing Tip: Write this section LAST, after all other sections are complete.

2. Introduction/Background

Purpose: Establish context and frame the research question.

Contents:

  • Problem statement or research question
  • Why this research matters (business/technical impact)
  • Scope boundaries (what IS and IS NOT covered)
  • Brief overview of approach taken

Length: 200-500 words

3. Methodology

Purpose: Enable reproducibility and establish credibility.

Contents:

  • Data sources consulted (with dates accessed)
  • Search strategies and queries used
  • Selection criteria for sources
  • Tools and techniques employed
  • Limitations of the methodology

Example Format:

### Data Collection
- Primary sources: [List with access dates]
- Search queries: [Exact queries used]
- Time range: [Date boundaries for research]

### Analysis Approach
- [Describe analytical framework]
- [Note any tools or models used]

4. Findings

Purpose: Present discovered facts objectively, without interpretation.

Contents:

  • Organized by theme, source type, or chronology
  • Each finding clearly attributed to source
  • Quantitative data in tables/charts when applicable
  • Direct quotes for critical evidence

Structure Options:

  • Thematic: Group by topic or category
  • Comparative: Side-by-side analysis of alternatives
  • Chronological: Timeline of developments
  • Source-based: Organized by information source

5. Analysis

Purpose: Interpret findings and extract meaning.

Contents:

  • Patterns and trends identified
  • Contradictions or gaps in evidence
  • Implications of findings
  • Comparison with existing knowledge
  • Confidence levels for conclusions

Analysis Framework:

### Pattern Analysis
[What recurring themes emerge?]

### Gap Analysis
[What questions remain unanswered?]

### Confidence Assessment
- High confidence: [Findings with strong evidence]
- Medium confidence: [Findings with partial evidence]
- Low confidence: [Tentative findings requiring validation]

6. Conclusions

Purpose: Synthesize analysis into actionable insights.

Contents:

  • Direct answers to research questions
  • Prioritized recommendations (if applicable)
  • Suggested next steps or future research
  • Final assessment of confidence

Format:

### Key Conclusions
1. [Most important conclusion]
2. [Second conclusion]
3. [Third conclusion]

### Recommendations
1. [Priority 1 action item]
2. [Priority 2 action item]

### Future Research Directions
- [Unanswered questions to explore]

7. References

Purpose: Enable verification and further exploration.

Contents:

  • All sources cited in the report
  • URLs with access dates
  • Proper attribution for all quoted material

Citation Formatting Guidelines

In-Text Citations

Use numbered references in square brackets for inline citations:

Recent studies indicate a 40% improvement in efficiency [1]. This aligns with
earlier findings on automation benefits [2, 3].

For direct quotes, include page numbers or section identifiers:

According to the official documentation, "the system supports up to 10,000
concurrent connections" [4, Section 3.2].

Reference List Format

Use a consistent format for all references:

Web Sources:

[1] Author/Organization. "Article Title." Website Name. URL. Accessed: YYYY-MM-DD.

Academic Papers:

[2] Author(s). "Paper Title." Journal/Conference Name, Year. DOI/URL.

Documentation:

[3] "Document Title." Product Name Documentation, Version X.X. URL. Accessed: YYYY-MM-DD.

News Articles:

[4] Author. "Headline." Publication Name, Date Published. URL.

Citation Best Practices

  1. Always include access dates for web sources (content may change)
  2. Prefer primary sources over secondary reports
  3. Note version numbers for software documentation
  4. Archive volatile sources when possible (use archive.org links)
  5. Verify link validity before finalizing report

Writing Style Recommendations

Style Selection Guide

AudienceStyleCharacteristics
ExecutivesExecutiveConcise, outcome-focused, minimal jargon
Technical TeamsTechnicalDetailed, precise terminology, includes code/data
Academic/ResearchAcademicFormal, extensive citations, methodological rigor
General StakeholdersBalancedClear explanations, moderate detail, accessible

Executive Style

Characteristics:

  • Lead with conclusions and recommendations
  • Use bullet points liberally
  • Limit technical jargon; define necessary terms
  • Focus on business impact and ROI
  • Keep paragraphs short (3-4 sentences max)

Example:

## Key Finding: Cloud Migration Reduces Costs by 35%

**Bottom Line**: Migrating to cloud infrastructure will reduce operational
costs by $2.4M annually while improving system reliability.

**Recommended Action**: Approve Phase 1 migration by Q2 2025.

**Risk Level**: Low - Similar migrations have 94% success rate.

Technical Style

Characteristics:

  • Include implementation details
  • Use precise technical terminology
  • Provide code samples, configurations, or specifications
  • Document edge cases and limitations
  • Include performance metrics and benchmarks

Example:

## Implementation: Rate Limiting Configuration

The API gateway implements token bucket rate limiting with the following
parameters:

| Parameter | Value | Rationale |
|-----------|-------|-----------|
| Bucket Size | 1000 | Handles burst traffic |
| Refill Rate | 100/sec | Sustainable throughput |
| Key Strategy | IP + User ID | Prevents abuse while supporting legitimate use |

rate_limiter = TokenBucket( capacity=1000, refill_rate=100, key_func=lambda req: f"{req.ip}:{req.user_id}" )

Academic Style

Characteristics:

  • Formal third-person voice
  • Extensive literature review
  • Detailed methodology documentation
  • Statistical rigor where applicable
  • Acknowledge limitations explicitly

Example:

## Literature Review

Previous research in automated code review systems has demonstrated
significant potential for defect detection. Smith et al. (2023) reported
a 23% reduction in production defects when implementing static analysis
tools [1]. However, Johnson and Lee (2024) note that these gains are
contingent upon proper configuration and team adoption [2].

The present study extends this work by examining the integration of
large language models into the review pipeline, an approach not
addressed in prior literature.

General Guidelines (All Styles)

  1. Active voice preferred: "The team implemented" not "It was implemented by the team"
  2. Specific over vague: "37% increase" not "significant increase"
  3. Present tense for findings: "The data shows" not "The data showed"
  4. Consistent terminology: Choose one term and use it throughout
  5. Avoid hedging excess: Limit "may," "might," "could possibly"

Quality Checklist Before Submission

Structure Verification

  • All seven standard sections present (or justified omission noted)
  • Executive summary can stand alone
  • Logical flow from introduction to conclusions
  • Section lengths appropriate to content importance
  • Headers and subheaders create clear hierarchy

Content Quality

  • Research question clearly stated and answered
  • All claims supported by cited evidence
  • Findings and analysis clearly separated
  • Contradictory evidence acknowledged
  • Confidence levels stated for conclusions
  • Limitations explicitly documented

Citation Integrity

  • All sources cited in reference list
  • All references cited in text
  • URLs verified as accessible
  • Access dates included for web sources
  • No broken or placeholder citations

Writing Quality

  • Consistent writing style throughout
  • Technical terms defined on first use
  • No unexplained acronyms
  • Spell-check completed
  • Grammar review completed
  • Sentence length varied (not all long or all short)

Formatting

  • Consistent heading styles
  • Tables and figures numbered and titled
  • Code blocks properly formatted
  • Bullet points parallel in structure
  • Page breaks at logical points (if applicable)

Final Review

  • Report answers the original research question
  • Recommendations are actionable
  • Nothing critical missing from scope
  • Appropriate length for audience and purpose
  • Ready for intended audience

Examples of Report Sections

Example: Executive Summary

## Executive Summary

This report evaluates three cloud database solutions for the customer
analytics platform migration: AWS Aurora, Google Cloud Spanner, and
Azure Cosmos DB.

**Key Findings**:
- AWS Aurora offers the lowest total cost of ownership ($145K/year)
- Google Cloud Spanner provides superior global consistency guarantees
- Azure Cosmos DB integrates best with existing Microsoft infrastructure
- All three solutions meet performance requirements (< 50ms p99 latency)

**Recommendation**: Proceed with AWS Aurora for Phase 1, with architecture
designed to allow future multi-cloud expansion.

**Timeline**: Implementation achievable within Q2 2025 with existing team.

**Confidence Level**: High - Based on proof-of-concept testing and vendor
consultations.

Example: Methodology Section

## Methodology

### Research Approach

This analysis employed a mixed-methods approach combining:
1. Vendor documentation review
2. Technical proof-of-concept testing
3. Industry analyst report analysis
4. Peer organization interviews

### Data Sources

| Source Type | Sources Consulted | Date Range |
|-------------|-------------------|------------|
| Vendor Docs | AWS, GCP, Azure official documentation | Dec 2024 |
| Analyst Reports | Gartner, Forrester database evaluations | 2024 |
| Technical Tests | Internal POC environment | Dec 15-22, 2024 |
| Interviews | 3 peer organizations (anonymized) | Dec 2024 |

### Evaluation Criteria

Solutions were evaluated against weighted criteria:
- Performance (30%): Latency, throughput, scalability
- Cost (25%): TCO over 3 years including migration
- Reliability (20%): SLA guarantees, disaster recovery
- Integration (15%): Compatibility with existing stack
- Vendor Support (10%): Documentation, support quality

### Limitations

- POC testing limited to 72-hour duration
- Cost projections based on current pricing (subject to change)
- Interview sample size limits generalizability

Example: Findings Section

## Findings

### Performance Comparison

All three solutions demonstrated acceptable performance for the target
workload of 10,000 queries per second:

| Solution | Avg Latency | P99 Latency | Max Throughput |
|----------|-------------|-------------|----------------|
| AWS Aurora | 12ms | 45ms | 15,000 QPS |
| Cloud Spanner | 15ms | 42ms | 18,000 QPS |
| Cosmos DB | 18ms | 48ms | 12,000 QPS |

*Source: Internal POC testing, December 2024 [1]*

### Cost Analysis

Three-year total cost of ownership analysis:

**AWS Aurora**: $435,000
- Compute: $180,000
- Storage: $95,000
- Data transfer: $85,000
- Support: $75,000

**Google Cloud Spanner**: $520,000
- [Detailed breakdown...]

**Azure Cosmos DB**: $485,000
- [Detailed breakdown...]

*Source: Vendor pricing calculators and enterprise discount estimates [2, 3, 4]*

Example: Analysis Section

## Analysis

### Cost-Performance Trade-offs

While AWS Aurora offers the lowest TCO, Cloud Spanner's 20% higher cost
delivers measurably better global consistency. For applications requiring
strong consistency across regions, this premium may be justified.

The cost difference primarily stems from:
1. Cloud Spanner's TrueTime infrastructure overhead
2. AWS Aurora's more aggressive reserved instance discounts
3. Different approaches to cross-region replication

### Risk Assessment

| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| Vendor lock-in | High | Medium | Abstract data layer |
| Price increases | Medium | Medium | 3-year commitment |
| Service outage | Low | High | Multi-region deployment |

### Confidence Assessment

**High Confidence**:
- Performance meets requirements (validated via POC)
- AWS Aurora is most cost-effective option

**Medium Confidence**:
- 3-year cost projections (pricing may change)
- Integration complexity estimates

**Low Confidence**:
- Long-term vendor roadmap alignment

Example: References Section

## References

[1] Internal Engineering Team. "Database POC Test Results." Internal
    Documentation. December 22, 2024.

[2] Amazon Web Services. "Amazon Aurora Pricing." AWS Documentation.
    https://aws.amazon.com/aurora/pricing/. Accessed: December 20, 2024.

[3] Google Cloud. "Cloud Spanner Pricing." Google Cloud Documentation.
    https://cloud.google.com/spanner/pricing. Accessed: December 20, 2024.

[4] Microsoft Azure. "Azure Cosmos DB Pricing." Azure Documentation.
    https://azure.microsoft.com/pricing/details/cosmos-db/.
    Accessed: December 20, 2024.

[5] Gartner. "Magic Quadrant for Cloud Database Management Systems."
    Gartner Research, November 2024. (Subscription required)

[6] Smith, J. and Chen, L. "Comparative Analysis of Distributed Databases."
    Proceedings of VLDB 2024. DOI: 10.14778/example.

Integration with Research Workflow

This skill integrates with the broader research workflow as follows:

Research Request → Data Collection → Analysis → [REPORT WRITING] → Verification → Delivery
                                                      ↑
                                                 This Skill

Inputs Expected:

  • Completed research findings (from sub-agents or direct research)
  • Original research request/questions
  • Source materials and citations
  • Any constraints (length, audience, format)

Outputs Produced:

  • Formatted report following structure template
  • Complete reference list
  • Executive summary for quick consumption

Quality Gates:

  • Report must pass quality checklist before marking complete
  • All citations must be verifiable
  • Conclusions must trace back to evidence in findings

平台分布

Codex

37.08%
按下载量换算62

Claude

29.29%
按下载量换算49

Cursor

18.36%
按下载量换算31

Gemini CLI

8.94%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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