company-intel skill

Purpose

Research engine that builds deep, structured understanding of companies, industries, and competitor sets. Produces a stable output format that you can hand off to other skills and agents to generate battlecards, SWOT analyses, positioning statements, PESTEL assessments, market sizing, and workshop content.

This is not a generic encyclopedia lookup. Every section pushes toward commercial understanding, product implications, and actionable intelligence. The output is a research primitive — structured data other skills consume — not a final deliverable.

Input

Works best with: The research target: a company, an industry, or a set of competitors. Also useful: The downstream use (battlecard, SWOT, positioning, market sizing) so the research emphasizes the right lenses, plus any constraints on depth or recency.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The workflow opens by asking for the target and what the intel will feed.

Example invocation: Research Figma and its top 3 competitors — output feeds a positioning workshop next week.

Key Concepts

Four Entry Points

The skill auto-detects entry point from the user's input. If ambiguous, ask one clarifying question: "Is this about a specific company, an industry, or a set of competitors?"

Single Company — User names a company (e.g., "Helix Motion Systems," "Brightwater Biologics" — or any real company). Produce the full 11-section output for that company.

Industry/Sector — User names an industry, sector, or niche (e.g., "clinical data management," "embedded finance," "upstream oil and gas"). Establish broad industry context, narrow into the segment, and connect findings to PM implications. Use the same 11-section structure adapted for sector-level analysis.

Named Competitor Set — User names 2-5 companies (e.g., "Compare Helix Motion, Northfield Automation, and Corvid Industrial"). Produce individual 11-section outputs for each company, then add a Section 12: Cross-Company Comparison that synthesizes across the set.

Discover Competitors — User names a company plus the word "competitors" (e.g., "helix-motion.com competitors" or "Helix Motion Systems competitors"). The skill:

1. Researches the named company first — enough to understand what it does, who it serves, and what market it plays in (a lightweight pass through Lenses 1-4)
2. Identifies 3-5 likely competitors based on that research, citing why each is a competitor (direct, adjacent, substitute, or emerging disruptor)
3. Presents the list for confirmation: "Based on my research, [Company]'s closest competitors appear to be [A, B, C, D, E]. Want me to run the full competitor set on these, or adjust the list first?"
4. Once confirmed, proceeds with the Named Competitor Set flow — full 11-section output for each company plus Section 12 cross-company comparison

The user can also provide a URL instead of a company name (e.g., "helix-motion.com"). The skill should resolve the URL to the company, research accordingly, and proceed.

Seven Research Lenses

These lenses structure all analysis. Apply every lens to every entry point.

Lens 1 — Financial Landscape and Business Outcomes How the entity makes money. Major revenue streams and cost drivers, margin pressures, growth levers, retention and expansion dynamics, capital intensity, seasonal or cyclical patterns, major risks to performance.

Lens 2 — Market Offer and Business Model How the entity creates and captures value. Target markets, buyers, users, influencers, administrators, and blockers. How segments differ. Multi-sided or multi-stakeholder dynamics.

Lens 3 — Product Portfolio and Product Outcomes Major offers, product families, services, platforms, channels. Bundled solutions, ecosystem plays. Digital versus human-assisted components. Legacy versus emerging offers. Distinction between business line, offer, product, feature set, service layer, and enabling platform.

Lens 4 — Competitive Dynamics Direct competitors, adjacent competitors, substitutes, emerging disruptors. Where differentiation is won or lost.

Lens 5 — Rising Trends and Strategic Concerns Market trends, regulatory forces, technology shifts (especially AI and automation), operational constraints, buyer expectation changes, threats from consolidation or commoditization.

Lens 6 — How Product Management Works Here Product-led vs sales-led vs service-led behavior. Centralized vs federated product structures. Platform vs solution orientation. Roadmap and innovation posture. Compliance or governance overhead. Discovery maturity, data maturity, experimentation maturity, AI maturity. Cross-functional friction. Label inferences clearly.

Lens 7 — Strategic Signals Three signal types — always check all three:

• Patent activity: Recent filings and grants via patent databases. Technology domains, R&D clusters, gaps between patent activity and public product narrative.
• Hiring signals: Roles open in volume, skills and tools in job descriptions, seniority being hired, language that reveals product culture (discovery, outcomes, AI-native, regulatory, experimentation).
• Leadership changes: C-suite and product leadership arrivals or departures in the last 12-18 months. Origin of new leaders (platform companies, consulting, competitors). New CPO, CTO, or CDO roles created, eliminated, or restructured. Board-level changes.

Sharp heuristics for reading the product organization specifically:

• CPO/VP Product tenure: a new product leader hired in the last 12-18 months almost always means the prior approach failed — read what they were hired from (platform company? consultancy? competitor?) as the intended correction.
• PM job postings as culture documents: how they define the PM role — outcome language vs. feature/roadmap language, discovery expectations, who PMs report to — reveals how product actually works there better than any About page.
• Employee-review themes: clusters around "roadmap chaos," "priorities change weekly," or "product vs. engineering tension" are Lens 6 evidence — community-tier confidence, labeled, but often the earliest honest signal.

Reading organizational distress (optional deepening, for engagement or partnership prep). When the intel supports a conversation with the company — a partnership, a sales motion, a job interview — add the distress read: what's the most likely presenting problem (what they'd say is wrong) versus the probable underlying problem (what the evidence suggests is actually wrong)? Was there a trigger event — missed guidance, a failed launch, a reorg, a new executive inheriting a mess? Calibrate the distress level: doing fine and optimizing → knows something's wrong → in trouble and doesn't know it yet. Label the whole read as Inference; it's the most useful and least certain section in the file.

Key Distinctions to Maintain

Always be disciplined about these — collapsing them produces shallow analysis:

• market vs segment
• buyer vs user
• product vs service
• business outcome vs product outcome vs output
• strategy vs tactics
• discovery vs delivery
• platform vs application
• signal vs assumption
• revenue growth vs market share growth vs customer lifetime value improvement vs cost reduction

Tensions Worth Surfacing

Highlight conflicts and tradeoffs wherever they appear:

• growth vs compliance
• scale vs customization
• digital self-service vs high-touch service
• standardization vs domain-specific workflow
• innovation vs legacy burden
• AI ambition vs governance reality
• customer value vs internal efficiency
• short-term revenue vs long-term platform investment

Why This Works

• Web-grounded: Uses live search, not training-data recall — output includes citations
• PM-native: Every section connects to product management implications, not just business facts
• Composable: Stable output format that downstream skills can parse and consume
• Repeatable: Same input next quarter produces fresh intel — the delta is the story
• Signal-driven: Strategic signals (patents, hiring, leadership) are often the most honest data available — they reveal what a company is actually doing, not what it says it's doing

Anti-Patterns

• Not a Wikipedia summary: Push past "what they do" to "what this means for product decisions"
• Not financial analysis: Focus is product strategy and commercial dynamics, not valuation or stock picks
• Not a prompt generator: The output is actual research with citations, not prompts for a future session
• Not a one-time exercise: Design for quarterly refresh — run it again, compare the delta

Research Expectations

Use web search actively. This skill requires live data gathering, not recall from training data. Search for and cite:

• Investor relations materials, annual reports, earnings transcripts
• Company product pages and official strategy pages
• Regulatory disclosures and filings
• Patent databases (Google Patents, USPTO)
• Company careers pages and job aggregators (LinkedIn, Indeed, Glassdoor)
• Executive appointment announcements and leadership change coverage
• Industry analysts (Gartner, Forrester, IDC) and reputable news coverage

Cite sources. Every factual claim should include a source. Use the library's canonical evidence labels from autonomous-investigation: Fact (source-supported), Inference (evidence-based interpretation), Assumption (working guess). When you're inferring — especially on Lens 6 (PM culture) and Lens 7 (strategic signals) — show the basis: "Inference based on [evidence]."

Source priority ladder. Primary (filings, earnings calls, investor docs) → credible secondary (major business press, trade publications) → community (Glassdoor, review sites, forums — lower confidence) → inferred signals (job postings, announcements). State where each claim sits.

Recency matters. Prioritize sources from the last 12-24 months. Flag anything older than 18 months explicitly.

Do not sanitize. Brutal product reviews, public criticism of leadership, employee accounts of roadmap chaos — it all belongs in the file, labeled and sourced. Intelligence that flatters the subject is marketing; the reader needs the real picture.

Application

Step 1: Detect Entry Point

Determine from user input:

• Single company → proceed to Step 2 with one entity
• Industry/sector → proceed to Step 2, adapt sections for sector-level analysis
• Named competitor set (2-5 companies listed) → proceed to Step 2 for each company, then Step 3
• Discover competitors (one company + "competitors") → proceed to Step 1b, then Step 2 for each, then Step 3
• URL provided (e.g., "helix-motion.com") → resolve to company name, then detect entry point from any additional context

If ambiguous, ask one question: "Is this about a specific company, an industry, or a set of competitors?"

If the user provides additional context (e.g., "I'm preparing for a client engagement with them" or "we compete with them in the SMB segment"), use that context to weight which lenses get deeper treatment.

Step 1b: Discover Competitors (when entry point is "discover competitors")

1. Research the named company using web search. Do a lightweight pass through Lenses 1-4 — enough to understand what the company does, who it serves, what market it plays in, and how it creates value.

1. Identify 3-5 likely competitors based on that research. For each, state:

• Company name
• Why it's a competitor (direct, adjacent, substitute, or emerging disruptor)
• One-sentence description of how it competes

1. Present the list for confirmation:

"Based on my research, [Company] is [brief description — what it does and who it serves].

Its closest competitors appear to be: