Name: Research Company
Author: TomsTools

據 AlphaMoat 統計,截至 2026-09,Research Company 累計下載 4,096 次、獲 3 個星標,在全部 5,078 個 AI 技能中排名第 2,469 位,在「專業領域」分類(465 個)中排名第 203 位。

累計安裝

275

累計下載

4096

星標

全站排名

#2,469

全站前 49% · 「專業領域」前 44%

平台	累計安裝	累計下載	星標	評論
ClawHub ↗	123	4062	3	-
SkillHub ↗	275	4096	3	—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

根據公司網址開展 B2B 公司研究，並生成 PDF 報告。

資料釋出於 2026-10-27

可以用來做什麼

圍繞上述任務組織內容
根據使用者提供的主題或物件生成結果
必要時提示核對來源或執行狀態

什麼時候用

需要完成根據公司網址開展 B2B 公司研究時
希望把相關步驟集中在同一工作流中時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

請根據我提供的內容，根據公司網址開展 B2B 公司研究。

先說明需要哪些輸入，再給出結果或後續步驟。

月度資料趨勢

累計下載4096

星標3

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份	累計下載	環比	星標
2026-09	4096	—	3
2026-08	3947	+7.7%	3
2026-07	3664	+10.8%	3
2026-06	3308	+5.5%	3
2026-05	3137	—	3
2026-03	2791	—	3

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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

展開全部(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