Ai strategy researcher
Skill shekerkamma/cc-best-practice/.claude/skills/ai-strategy-researcher
Personal fork of claude-code-best-practice with tokyo-time skills, hooks demo, and presentation work
npx -y skills add shekerkamma/cc-best-practice --skill ai-strategy-researcherAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Research and generate comprehensive AI business strategy reports as Word documents. Covers market signals, VC theses, vertical analysis, competitive landscape, unit economics, and operational playbooks. Use when the user wants AI market intelligence, strategy analysis, or business planning documents.
SKILL.md
5.5 KB, as published. Nobody here has run it
AI Strategy Researcher Skill
Research AI market signals and generate comprehensive business strategy documents as Word (.docx) files.
Task
Given a topic, vertical, or strategic question, conduct deep research across multiple sources and produce a professional Word document with full references.
Research Framework
Execute research in this order, running parallel searches where possible:
Phase 1: Market Signals (parallel searches)
- VC Theses — Search for latest investment theses from Sequoia, Emergence Capital, a16z, Bessemer, Y Combinator related to the topic
- Major Moves — Search for recent fundraises, acquisitions, JVs, and strategic partnerships (OpenAI, Anthropic, Google, Microsoft)
- Market Sizing — Search for TAM/SAM estimates from analyst reports, investor decks, and industry publications
Phase 2: Competitive Intelligence (parallel searches)
- Proof Points — Search for companies already winning in the space (ARR, valuation, growth metrics)
- Competitive Landscape — Map incumbents, startups, and model providers entering the space
- Failure Analysis — Search for companies that failed or pivoted, and why
Phase 3: Operational Intelligence (parallel searches)
- Unit Economics — Gross margins, revenue per employee, pricing models, COGS structure
- Go-to-Market — How successful companies acquired customers, partnership strategies
- Playbooks — Operational frameworks from VCs and successful founders
Phase 4: Framework Application
- Apply Sequoia's Copilot vs. Autopilot framework to the topic
- Apply Emergence Capital's Intelligence vs. Judgement framework to identify AI readiness
- Identify the Mirage PMF risk specific to this vertical/topic
- Define a North Star Metric appropriate to the vertical
Key Sources to Check
Always search these authoritative sources:
| Source | What to look for |
|---|---|
| Sequoia Capital blog | "Services: The New Software" thesis, vertical analysis |
| Emergence Capital | AI-Native Services Playbook, portfolio companies |
| Y Combinator | Request for Startups, batch companies in the space |
| Bessemer Venture Partners | Cloud/AI indices, pricing playbooks |
| a16z | Market maps, industry analyses |
| OpenAI blog | Deployment Company updates, Frontier Alliances |
| Anthropic blog | Enterprise JV updates, partner announcements |
| TechCrunch | Funding rounds, startup coverage |
| Fortune | Executive interviews, strategic analysis |
| Bloomberg | Financial data, deal structures |
Document Generation
After research is complete, generate a Word document using python-docx:
Required Sections
- Cover Page — Title, subtitle, date, source attribution
- Table of Contents — All sections and subsections
- Executive Summary — 5 key findings with market signals
- Market Signal Analysis — What just happened and why it matters
- Macro Thesis — VC frameworks applied to the topic
- Market Sizing & Vertical Analysis — TAM with tables
- Proof Points — Companies already winning with metrics
- Operational Playbook — How to build/execute
- Unit Economics — Margins, pricing, key metrics
- Competitive Moats — Defensibility framework
- Risk Analysis — Mirage PMF and failure modes
- Strategic Framework — Decision matrix for the user
- Competitive Landscape — Positioning map
- References & Sources — ALL URLs organized by category
Document Standards
- Use
Calibrifont,Pt(11)body, colored headings - Professional tables using
Light Grid Accent 1style - Block quotes for key insights (italic, indented)
- Bullet points with bold prefixes for scanability
- All references include source name, article title, URL, and date
- Minimum 30 referenced URLs across categories
File Output
- Save to project root as:
{topic-slug}-strategy-{month}{year}.docx - Example:
ai-native-insurance-strategy-may2026.docx - Clean up the Python generator script after document creation
Python Dependencies
The document generator requires python-docx. Install if needed:
pip install python-docx
Quality Checklist
Before delivering the document, verify:
- All 13 sections present
- 15+ formatted tables with data
- 30+ referenced URLs with source attribution
- Copilot vs. Autopilot framework applied
- Intelligence vs. Judgement analysis included
- Mirage PMF risks identified
- North star metric defined
- Cover page with date and source attribution
- Professional formatting throughout
Notes
- Always run parallel WebSearch calls where possible to minimize research time
- Prefer primary sources (VC blogs, company announcements) over news aggregators
- Include both successful and failed companies for balanced analysis
- Convert all relative dates to absolute dates in the document
- The document should be investor-ready and presentable to stakeholders