Awesome-openclaw-skills research-company

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.

install

source · Clone the upstream repo

git clone https://github.com/sundial-org/awesome-openclaw-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/sundial-org/awesome-openclaw-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/research-company" ~/.claude/skills/sundial-org-awesome-openclaw-skills-research-company && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/sundial-org/awesome-openclaw-skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/research-company" ~/.openclaw/skills/sundial-org-awesome-openclaw-skills-research-company && rm -rf "$T"

manifest: skills/research-company/SKILL.md

source content

Company Research

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

Workflow

Research the company (web fetch + searches)
Build JSON data structure
Generate PDF via
```
scripts/generate_report.py
```
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):

{
  "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

# 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