mcpField
R
research-company

research-company

github.com/kirkluokunMarketing & growth6656 installsUpdated 32w ago
Repository

About research-company

research-company is a skill for producing styled PDF company research reports from a company URL. It guides research, structured data preparation, PDF generation, and delivery, with an emphasis on cited evidence and avoiding unsupported claims.

Key features

  1. 01Research company sites and web results
  2. 02Structure findings as JSON
  3. 03Generate styled PDF reports
  4. 04Cite evidence and note information gaps

Use cases

  1. 01Prepare company background for sales research
  2. 02Compile company profiles from public sources

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill research-company

After installing, type/research-companyin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

_meta.json
alibaba_research_data.json
references/
data-schema.md
scripts/
generate_report.py

Details

From the project README

Company Research

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

Workflow

  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):

{
  "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

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