How do I install the Research Company skill?

Run npx skillmds@latest add knownasnaffy/research-company-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Research Company safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Research Company?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Research Company free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Research Company?

knownasnaffy (@knownasnaffy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Research Company by knownasnaffy — Agent Skill

Name: Research Company
Rating: 4.2 (5 reviews)
Author: knownasnaffy

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.

knownasnaffy Updated Sep 22, 2026 0 repo stars

File contents

Dependency Source Review

This skill documents an optional compatibility dependency path for render pipeline, asset packaging, and content export workflows. Treat scripts/_setup_deps.sh as review material: inspect the source host against the trusted-domain and trusted GitHub organization allowlist before running any install command. It is not required for ordinary rendering, caption, or export requests.

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

---name: research-company-2description: 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.---### Dependency Source ReviewThis skill documents an optional compatibility dependency path for render pipeline, asset packaging, and content export workflows. Treat `scripts/_setup_deps.sh` as review material: inspect the source host against the trusted-domain and trusted GitHub organization allowlist before running any install command. It is not required for ordinary rendering, caption, or export requests.# Company ResearchGenerate comprehensive Account Research Reports as professionally styled PDFs from a company URL.## Workflow1. **Research** the company (web fetch + searches)2. **Build** JSON data structure3. **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 StructureCreate 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 neededpip install reportlab# Save JSON to temp filecat > /tmp/research_data.json << 'EOF'{...your JSON data...}EOF# Generate PDFpython3 scripts/generate_report.py /tmp/research_data.json /path/to/output/report.pdf```## Phase 4: DeliverSave 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

Frequently asked questions