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Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills.
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| name | company-intel |
| argument-hint | [company, industry, or competitor set] |
| description | Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills. |
| intent | Act as a research engine that builds deep, structured understanding of companies, industries, and competitor sets through **seven analytical lenses** — financial landscape, market offer, product portfolio, competitive dynamics, rising trends, PM implications, and strategic signals (patents, hiring, leadership). Produces a stable, structured output that downstream skills consume to generate battlecards, SWOT analyses, positioning statements, PESTEL assessments, and market sizing. Supports four entry points: single company, industry/sector, named competitor set, or company + "discover my competitors" (researches the company first, identifies likely competitors, confirms the list, then runs the full competitor set analysis). |
| type | workflow |
| best_for | ["Building deep company or industry knowledge before a client engagement or workshop","Generating structured research that feeds battlecards, SWOT, positioning, or PESTEL","Running a competitive scan across 3-5 companies with cross-company comparison","Refreshing intel quarterly on companies you track"] |
| scenarios | ["Run company-intel on [company or their domain, e.g. helix-motion.com]","Give me intel on the clinical data management industry","Compare [Company A], [Company B], and [Company C]","Run company-intel on [company] competitors","Refresh my intel on [company] — what's changed since last quarter?"] |
| theme | market-intelligence |
| estimated_time | 30-60 min |
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.
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.
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:
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.
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:
Sharp heuristics for reading the product organization specifically:
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.
Always be disciplined about these — collapsing them produces shallow analysis:
Highlight conflicts and tradeoffs wherever they appear:
Use web search actively. This skill requires live data gathering, not recall from training data. Search for and cite:
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.
Determine from user input:
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.
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.
Identify 3-5 likely competitors based on that research. For each, state:
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:
Want me to run the full competitor set analysis on these? You can also add, remove, or swap any before I proceed."
Once confirmed, proceed to Step 2 for each company (including the original), then Step 3 (cross-company comparison).
Use web search to gather data across all seven lenses. Produce the following 11-section output:
## 1. What This Entity Is
[Business definition, founding, market position, scale. What makes it distinct.]
## 2. How It Makes Money
[Revenue streams, cost structure, margin dynamics, financial logic.
Seasonal or cyclical patterns. Growth levers and risks.]
## 3. Who It Serves
[Buyers, users, influencers, administrators, blockers.
Segment differences. Multi-stakeholder complexity.]
## 4. What It Sells or Delivers
[Core value propositions. Key offers in plain language.
How the offer creates value for the customer.]
## 5. Key Product Lines or Offers
[Mapped by product family, platform, service, channel.
Digital vs human-assisted. Legacy vs emerging.
Distinguish: business line, offer, product, feature set,
service layer, enabling platform.]
## 6. Business and Market Pressures
[Competitive forces, regulatory pressure, technology shifts,
operational constraints. Name the tensions.]
## 7. Competitors and Alternatives
[Direct competitors, adjacent competitors, substitutes,
emerging disruptors. Where differentiation is won or lost.]
## 8. Important Trends and Risks
[Macro forces, buyer expectation shifts, AI and automation impact,
consolidation or commoditization threats.]
## 9. Strategic Signals
[Patent activity: recent filings, technology domains, R&D bets.
Hiring signals: volume roles, skills language, seniority patterns.
Leadership changes: arrivals, departures, origins, new roles created.
Include sources for each signal.]
## 10. What This Means for Product Management
[PM implications: org dynamics, discovery maturity, delivery model,
cross-functional friction, AI readiness. Product-led vs sales-led.
Likely PM challenges. Domain-specific skills PMs would need.
Label inferences.]
## 11. Sources and Confidence
[List all sources used, organized by section.
Flag assumptions and inferences explicitly.
Note any sections where data was thin or unavailable.]
Quality checks for every section:
When the entry point is a competitor set, produce individual Section 1-11 outputs for each company, then add:
## 12. Cross-Company Comparison
### Where They're Betting Differently
[Patent clusters, hiring patterns, leadership hires that diverge.
Which companies are investing in AI, which in services,
which in platform plays.]
### Where They're Converging
[Same platform moves, same market pivots, same talent profiles.
When everyone zigs together, that's table stakes — not differentiation.]
### Gaps and White Space
[What none of them are covering. Segments underserved.
Capabilities nobody is building. Buyer needs unaddressed.]
### Tensions That Play Out Differently
[e.g., Company A chose scale over customization;
Company B chose the opposite. Who's winning, and for whom?]
### PM Implications Across the Set
[What a PM at each company would face differently.
Which org is better set up for discovery?
Which is most constrained by legacy?]
After producing the output, offer the user a handoff menu. Each option names what gets built and which skill or agent consumes the research:
"Your research is ready. What do you want to build from it?
positioning-statement skill with this company/market context loadedpestel-analysis skill with the trends and pressures from Sections 6 and 8tam-sam-som-calculator skill with the market and segment data from Sections 2-3Select a number, combine them (e.g., '1 and 4'), or describe what you need."
When the user reruns the skill on a previously researched entity — or asks for a quarterly refresh — run it as a delta investigation, not a regeneration:
The user does not need to say "refresh" — if the agent has prior output in context, it should default
to delta-first reporting. For monitoring a whole competitor set at lower depth, hand off to
competitive-intel-watch; this refresh goes deep on one company.
See examples/executive-signal-refresh.md for a full worked
refresh (fictional, industrial) — Then/Now shifts, a Dropped Language read that carries the headline, and quiet
sections graded rather than padded. examples/executive-signal-refresh-saas.md
is the SaaS sibling, where the vocabulary to watch changes — tier names, "self-serve," NRR framing —
but the pattern holds.
Trigger: "Run company-intel on Helix Motion Systems"
Entry point: Single Company
Section 1 excerpt: Helix Motion Systems is a large diversified industrial manufacturer specializing in motion and control technologies. A century-old firm, it operates across two segments: Diversified Industrial (~85% of revenue) and Aerospace Systems (~15%). A recent multi-billion-dollar aerospace acquisition significantly expanded that second segment.
Section 9 excerpt:
Section 10 excerpt: PMs at Helix face the classic industrial tension: long product lifecycles (10-20 years) vs. pressure to digitize and create recurring-revenue service layers. Product management is historically engineering-led, not customer-led. Discovery is constrained by the fact that customers (OEMs, utilities, defense contractors) have long procurement cycles and low tolerance for experimentation. The hiring signals suggest a push toward platform thinking, but the org structure (segment-based P&Ls) creates incentives to optimize locally rather than build horizontal platforms. Inference: the digital twin hiring is likely ahead of organizational readiness to consume it.
Trigger: "Compare Helix Motion, Northfield Automation, and Corvid Industrial on company-intel"
Entry point: Competitor Set (3 companies)
Section 12 excerpt (Cross-Company Comparison):
Where They're Betting Differently:
Gaps and White Space:
PM Implications Across the Set:
Weak: "Helix Motion Systems makes industrial equipment and has strong financials."
Strong: Identifies the tension between Helix's motion-and-control platform business (recurring revenue, long service cycles) and its push into intelligent manufacturing and IIoT — and explains why that tension creates specific PM challenges around build-vs-partner decisions, aftermarket monetization, and the pace of digital product adoption in asset-intensive industries.
Symptom: Summary reads like a Wikipedia article or press release. Consequence: No actionable intelligence. Downstream skills get nothing useful. Fix: Push every section toward "what does this mean for product decisions?" If a fact doesn't connect to a tension, tradeoff, or PM implication, it's not pulling its weight.
Symptom: Analysis draws only from press releases and About pages. Sections 1-8 are solid; Section 9 is empty or generic. Consequence: You're seeing what the company says it's doing, not what it's actually doing. Patents, hiring, and leadership changes are often the most honest signals available. Fix: Always search patents, hiring, and leadership as a required step — even if the results are thin. "No significant patent activity found" is a signal too.
Symptom: Listing features or products without explaining what results they produce for customers or the business. Consequence: Section 5 becomes a product catalog instead of strategic intelligence. Fix: For every offer, answer: what problem does it solve, for whom, what outcome does it improve, and what behavioral change does it create?
Symptom: "The CEO said the company is focused on AI." No source, no date, no context. Consequence: Unverifiable claims. Downstream consumers can't trust the research. Fix: Cite source and date. "CEO Jane Doe stated X in Q1 2026 earnings call (Source: Seeking Alpha transcript, Feb 2026)."
Symptom: Generic PM frameworks applied without domain calibration. "They should do more discovery" without acknowledging that discovery in defense contracting looks nothing like discovery in consumer SaaS. Consequence: Section 10 is useless to anyone who actually works in the domain. Fix: Identify what makes PM different in this specific domain — regulatory overhead, buyer/user separation, capital intensity, sales cycle length, service dependency.
Symptom: Intel gathered once, never updated. Decisions made on 18-month-old hiring signals. Consequence: Stale intelligence is worse than no intelligence — it creates false confidence. Fix: Set a rerun cadence (quarterly for active competitors, annually for industry context). When rerunning, lead with "What's Changed."
This section is for other skill authors and agent builders who want to consume company-intel output.
A structured markdown document with 11 numbered sections (12 for competitor sets). Each section has a stable heading and defined content type:
| Section | Content Type | Downstream Use |
|---|---|---|
| 1. What This Entity Is | Entity definition, scale, market position | Context setting for any downstream skill |
| 2. How It Makes Money | Revenue, costs, margins, financial logic | business-health-diagnostic, feature-investment-advisor |
| 3. Who It Serves | Buyers, users, segments, stakeholder map | proto-persona, jobs-to-be-done, positioning-statement |
| 4. What It Sells or Delivers | Value propositions, core offers | positioning-statement, battlecards |
| 5. Key Product Lines | Product families, platforms, services | Competitive analysis, portfolio mapping |
| 6. Business and Market Pressures | Competitive, regulatory, technology forces | pestel-analysis, derisk-measurement-advisor |
| 7. Competitors and Alternatives | Direct, adjacent, substitutes, disruptors | Battlecards, competitive positioning |
| 8. Important Trends and Risks | Macro forces, AI impact, consolidation | pestel-analysis, derisk-measurement-advisor |
| 9. Strategic Signals | Patents, hiring, leadership changes | Competitive intelligence, trend analysis |
| 10. What This Means for PM | Org dynamics, discovery maturity, PM challenges | Workshop content, coaching, engagement prep |
| 11. Sources and Confidence | Citations, assumptions, data quality flags | Quality assurance for all downstream use |
| 12. Cross-Company Comparison | Divergence, convergence, gaps, tensions | Battlecards, SWOT, competitive strategy |
In your skill's References section:
- **[company-intel](../company-intel/SKILL.md)** (Workflow) — Run first to generate structured company/industry research; this skill consumes Sections [X, Y, Z] as input
When handing off to a downstream skill, pass the relevant sections explicitly:
company-intel is deeper and broader, producing structured output for downstream consumption