Name: Research Company
Author: TomsTools

据 AlphaMoat 统计,截至 2026-09,Research Company 累计下载 4,096 次、获 3 个星标,在全部 5,078 个 AI 技能中排名第 2,469 位,在「专业领域」分类(465 个)中排名第 203 位。

累计安装

275

累计下载

4,096

星标

全站排名

#2,469

全站前 49% · 「专业领域」前 44%

平台	累计安装	累计下载	星标	评论
ClawHub ↗	123	4,062	3	-
SkillHub ↗	275	4,096	3	—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

根据公司网址开展 B2B 公司研究，并生成 PDF 报告。

资料发布于 2026-10-27

可以用来做什么

围绕上述任务组织内容
根据用户提供的主题或对象生成结果
必要时提示核对来源或执行状态

什么时候用

需要完成根据公司网址开展 B2B 公司研究时
希望把相关步骤集中在同一工作流中时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

请根据我提供的内容，根据公司网址开展 B2B 公司研究。

先说明需要哪些输入，再给出结果或后续步骤。

月度数据趋势

累计下载4,096

星标3

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份	累计下载	环比	星标
2026-09	4,096	—	3
2026-08	3,947	+7.7%	3
2026-07	3,664	+10.8%	3
2026-06	3,308	+5.5%	3
2026-05	3,137	—	3
2026-03	2,791	—	3

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

2.6 KB · ClawHub v1.0.0

---
name: research-company
description: B2B company research producing professional PDF reports. Use when asked to research a company, analyze a business, create an account profile, or generate market intelligence from a company URL. Outputs a beautifully formatted, downloadable PDF report.
---

# Company Research

Generate comprehensive Account Research Reports as professionally styled PDFs from a company URL.

## Workflow

展开全部(共 85 行)


1. **Research** the company (web fetch + searches)
2. **Build** JSON data structure
3. **Generate** PDF via `scripts/generate_report.py`
4. **Deliver** PDF to user

## Phase 1: Research (Parallel)

Execute these searches concurrently to minimize context usage:

```
WebFetch: [company URL]
WebSearch: "[company name] funding news 2024"
WebSearch: "[company name] competitors market"
WebSearch: "[company name] CEO founder leadership"
```

Extract from website: company name, industry, HQ, founded, leadership, products/services, pricing model, target customers, case studies, testimonials, recent news.

## Phase 2: Build Data Structure

Create JSON matching this schema (see `references/data-schema.md` for full spec):

```json
{
  "company_name": "...",
  "source_url": "...",
  "report_date": "January 20, 2026",
  "executive_summary": "3-5 sentences...",
  "profile": { "name": "...", "industry": "...", ... },
  "products": { "offerings": [...], "differentiators": [...] },
  "target_market": { "segments": "...", "verticals": [...] },
  "use_cases": [{ "title": "...", "description": "..." }],
  "competitors": [{ "name": "...", "strengths": "...", "differentiation": "..." }],
  "industry": { "trends": [...], "opportunities": [...], "challenges": [...] },
  "developments": [{ "date": "...", "title": "...", "description": "..." }],
  "lead_gen": { "keywords": {...}, "outreach_angles": [...] },
  "info_gaps": ["..."]
}
```

## Phase 3: Generate PDF

```bash
# Install if needed
pip install reportlab

# Save JSON to temp file
cat > /tmp/research_data.json << 'EOF'
{...your JSON data...}
EOF

# Generate PDF
python3 scripts/generate_report.py /tmp/research_data.json /path/to/output/report.pdf
```

## Phase 4: Deliver

Save PDF to workspace folder and provide download link:
```
[Download Company Research Report](computer:///sessions/.../report.pdf)
```

## Quality Standards

- **Accuracy**: Base claims on observable evidence; cite sources
- **Specificity**: Include product names, metrics, customer examples
- **Completeness**: Note gaps as "Not publicly available"
- **No fabrication**: Never invent information

## Resources

- `scripts/generate_report.py` - PDF generator (uses reportlab)
- `references/data-schema.md` - Full JSON schema with examples