Imported from arjundrath-star/klade-analyst (clients/_archived/reid/skills/industry-deep-dive/SKILL.md). Install upstream with npx skills add arjundrath-star/klade-analyst --skill industry-deep-dive. Copyright stays with the author.

industry-deep-dive

Produce a comprehensive industry report suitable for institutional distribution. This is the foundational research that informs sector allocation, thematic investing, and due diligence on individual names. Think: a sell-side industry initiation report — not a Wikipedia summary.

Trigger

"Industry deep dive on [SECTOR/INDUSTRY]"
"Full industry analysis for [INDUSTRY]"
"Market sizing for [INDUSTRY]"
"Competitive landscape in [SECTOR]"
"Value chain analysis for [INDUSTRY]"
"Industry report on [THEME/SECTOR]"

Inputs

Industry / Sector: name, GICS code, or thematic description (required)
Geography: US (default), Global, Europe, APAC, or specific country
Time horizon: 3-year (default), 5-year, or 10-year forecast window
Focus areas: market sizing, competitive dynamics, regulation, value chain, all (default: all)
Depth: standard (20-25 pages) / comprehensive (30-40 pages) — default: standard
Comparison period: historical lookback years (default: 5)

Dependencies

financial-data-api — data source stack (see ../financial-data-api/SKILL.md)
comps-builder — for peer valuation context within the industry
company-brief — for individual company snapshots referenced in landscape

⚠️ DATA SOURCING MANDATE (NON-NEGOTIABLE)

SEC EDGAR XBRL (PRIMARY for company financials referenced in the report):
- Use for revenue, margins, segment data of key industry players
- web_fetch("https://data.sec.gov/api/xbrl/companyfacts/CIK{CIK_PADDED_10}.json")
FRED API (PRIMARY for macro/industry-level data):
- Industry production indices, GDP components, employment data, interest rates
- web_fetch("https://api.stlouisfed.org/fred/series/observations?series_id={ID}&api_key=${FRED_API_KEY}&file_type=json")
Census Bureau / BLS / BEA (PRIMARY for government statistics):
- web_fetch("https://api.census.gov/data/...") for industry revenue, establishment counts
- BLS for employment, wages, productivity in the sector
web_search (SUPPLEMENTARY for market research and qualitative intelligence):
- Use for: TAM/SAM estimates from research firms (Gartner, IDC, McKinsey, BCG), qualitative trends, news, regulatory developments
- Always attribute: "According to [Source], [Year]..."
- Cross-reference market sizing estimates across at least 3 sources
Massive.com API (for public company market data within the industry):
- Aggregate market caps, sector performance, index comparisons

Methodology

Phase 1: Industry Definition & Scoping (2-3 pages)

Step 1: Define the industry boundary precisely

web_search("[INDUSTRY] GICS classification sub-industries SIC codes")
web_search("[INDUSTRY] NAICS codes industry definition scope")
web_search("[INDUSTRY] market definition what's included excluded")

Document:

GICS / SIC / NAICS codes that map to this industry
What IS included (core activities, adjacent activities counted)
What is NOT included (common misconceptions, adjacent sectors excluded)
How this industry intersects with adjacent sectors (supply chain linkage, customer overlap)

Step 2: Industry lifecycle stage assessment Classify as: Embryonic → Growth → Shakeout → Mature → Decline Provide evidence for classification: revenue growth rates, number of competitors over time, margin trends, M&A activity, innovation pace.

Phase 2: Market Sizing (3-5 pages)

Step 3: TAM / SAM / SOM framework

web_search("[INDUSTRY] total addressable market TAM 2024 2025 2030")
web_search("[INDUSTRY] market size revenue forecast CAGR")
web_search("[INDUSTRY] Gartner OR IDC OR McKinsey OR BCG market sizing")
web_search("[INDUSTRY] market size by region North America Europe Asia")
web_search("[INDUSTRY] market size by segment breakdown")

Build three estimates:

Top-down: Start from macro data (GDP component, industry % of total economy), work down
Bottom-up: Sum individual company revenues, estimate private/unlisted share, validate
Triangulation: Cross-reference at least 3 third-party market sizing reports

Present as:

Global TAM: $[X]B (CY[Year])
US/Regional SAM: $[X]B
CAGR: [X]% (historical 5-year) → [X]% (forecast period)
Market size by segment (revenue split with % of total)
Market size by geography (revenue split with % of total)

Step 4: Bottom-Up Market Sizing

The top-down TAM from research reports is a starting point, but bottom-up validation is what separates credible analysis from hand-waving. Build a unit-economics model independently:

web_search("[INDUSTRY] number of customers users subscribers installed base")
web_search("[INDUSTRY] average revenue per user ARPU average deal size")
web_search("[INDUSTRY] number of establishments businesses Census Bureau NAICS")
web_search("[INDUSTRY] penetration rate adoption percentage addressable")

Bottom-Up Framework:

Market Size = Addressable Customer Count × Average Revenue Per Customer × Penetration Rate

Step 1: Define the addressable customer count
  - B2B: Number of businesses in target segments (Census Bureau SUSB data by NAICS + employee size)
  - B2C: Population in target demographics (Census Bureau ACS data)
  - B2G: Number of government agencies/departments at target level

Step 2: Calculate average revenue per customer
  - Use public company revenue / customer count (from 10-K disclosures, investor presentations)
  - Cross-reference with industry surveys (trade associations, analyst reports)
  - Segment by customer tier if pricing varies significantly (enterprise vs SMB vs consumer)

Step 3: Estimate penetration rate
  - What % of addressable customers currently use this product/service?
  - What's the saturation ceiling? (Some industries max at 60-70%, not 100%)
  - What's the growth trajectory of penetration? (S-curve positioning)

Step 4: Calculate
  Current market: [Customers] × [ARPU] × [Current penetration]
  Addressable market: [Customers] × [ARPU] × [Saturation ceiling]
  Growth implied: [Addressable] − [Current] = headroom

Bottom-Up Validation Table:

Component	Value	Source	Confidence
Total addressable customers	[N]	[Census/BLS/specific source]	[High/Med/Low]
Current penetration rate	[X]%	[Source — survey, company data, estimate]	[High/Med/Low]
Average revenue per customer	$[X]	[Derived from public company data: Company/CIK]	[High/Med/Low]
Bottom-up market size	$[X]B	Calculated
Top-down market size (for comparison)	$[X]B	[Research firm, year]
Variance	[X]%

If variance between top-down and bottom-up exceeds 15%, explain the difference:

Private companies not captured in bottom-up? (estimate private share)
Different market definitions? (scope mismatch)
Geographic coverage difference?
Research firm using different ARPU assumptions?

Step 5: Growth decomposition Break growth into: volume growth + pricing power + mix shift + new product/service categories + geographic expansion. Quantify each component where data permits.

Phase 3: Competitive Landscape (5-8 pages)

Step 5: Market structure analysis

web_search("[INDUSTRY] market share leaders top companies 2024 2025")
web_search("[INDUSTRY] competitive landscape market concentration HHI")
web_search("[INDUSTRY] market share by company revenue")
web_search("[INDUSTRY] barriers to entry competitive moats")

Classify market structure:

Perfect competition (fragmented, commodity-like)
Monopolistic competition (many players, differentiated)
Oligopoly (3-7 dominant players, high barriers)
Monopoly/duopoly (1-2 players control >60%)

Compute and present:

CR4 (top 4 concentration ratio) and CR8
HHI (Herfindahl-Hirschman Index) if data permits
Revenue share table for top 10 players with 3-year trend

Step 6: Porter's Five Forces (quantified, not generic) For EACH force, provide specific evidence and rate 1-5:

Force	Rating	Key Evidence
Threat of New Entry	[1-5]	[Specific barriers: capital requirements $X, regulatory licenses, network effects, brand moats]
Supplier Power	[1-5]	[Concentration of suppliers, switching costs, input % of COGS]
Buyer Power	[1-5]	[Customer concentration, switching costs, price sensitivity]
Substitution Threat	[1-5]	[Specific substitutes, price-performance comparison]
Competitive Rivalry	[1-5]	[Number of competitors, growth rate, differentiation, exit barriers]

Step 7: Competitive positioning map Plot top 10 players on a 2x2 matrix (e.g., Scale vs. Specialization, Growth vs. Profitability, Innovation vs. Cost Leadership). Describe each quadrant and identify strategic groups.

Phase 4: Value Chain Mapping (3-5 pages)

Step 8: Map the full value chain — end-to-end

This is not a simple diagram — it's a complete economic map of how value flows from raw input to end customer. For each stage, you must identify: who captures value, how much, and why.

web_search("[INDUSTRY] value chain supply chain structure")
web_search("[INDUSTRY] upstream midstream downstream players")
web_search("[INDUSTRY] margin distribution across value chain")
web_search("[INDUSTRY] value chain economics cost structure breakdown")

Value Chain Template (complete for every industry deep dive):

Stage	Description	Key Players	Revenue at Stage	Gross Margin	Value Capture (% of end $)	Key Moat	Disruption Risk
1. Raw Input / Sourcing	[What enters the chain]	[2-3 named companies with market share]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High — why]
2. Processing / Manufacturing	[Transformation step]	[2-3 named companies]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High]
3. Assembly / Integration	[Value-add step]	[2-3 named companies]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High]
4. Distribution / Logistics	[How product reaches customer]	[2-3 named companies]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High]
5. Sales / End Customer	[Final transaction]	[2-3 named companies]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High]
6. Aftermarket / Services	[Post-sale value]	[2-3 named companies]	$[X]B	[X]%	[X]%	[Specific barrier]	[Low/Med/High]

Key players must be named companies — not "various manufacturers." Include CIK numbers for public companies so financials can be cross-referenced via EDGAR.

Margin validation: Where possible, validate margin profiles against actual company financials:

web_fetch("https://data.sec.gov/api/xbrl/companyfacts/CIK{CIK_PADDED_10}.json")

Use gross margin from the most recent annual filing. Flag if margin profile deviates significantly from the stated range.

Present as a visual flow:

[Raw Materials] → [Components/Processing] → [Assembly/Production] → [Distribution] → [End Customer] → [Aftermarket]
   Margin: X%         Margin: X%              Margin: X%           Margin: X%        Margin: X%        Margin: X%
   $/unit: $X         $/unit: $X              $/unit: $X           $/unit: $X        $/unit: $X        $/unit: $X
   Key: [Names]       Key: [Names]            Key: [Names]         Key: [Names]      Key: [Names]      Key: [Names]
   Moat: [Type]       Moat: [Type]            Moat: [Type]         Moat: [Type]      Moat: [Type]      Moat: [Type]

Step 9: Where value is migrating

Identify shifts in value capture over the past 5-10 years. Quantify with historical margin trends at each stage:

Value Migration	From	To	Evidence	Magnitude	Timeline
[Shift 1]	[Stage/player losing]	[Stage/player gaining]	[Specific margin data]	[X% shift]	[Started YYYY, expected through YYYY]
[Shift 2]	[Stage/player losing]	[Stage/player gaining]	[Specific margin data]	[X% shift]	[Timeline]

Common value migration patterns to check for:

Hardware → Software/SaaS (margin expansion at software layer)
Manufacturing → Services/Aftermarket (razor/razorblade model)
Distributors → D2C (disintermediation)
Generalists → Specialists (premium for domain expertise)
Physical → Digital (lower marginal cost, higher operating leverage)
Owned → Platform/marketplace (asset-light value capture)

Phase 5: Growth Drivers & Headwinds (3-5 pages)

Step 10: Secular growth drivers

web_search("[INDUSTRY] growth drivers trends 2025 2030")
web_search("[INDUSTRY] secular trends structural tailwinds")
web_search("[INDUSTRY] technology disruption innovation")

For each driver, provide:

Description (1-2 sentences)
Quantified impact (adds $XB to TAM, or +X% CAGR contribution)
Timeline (near-term 1-2 years, medium 3-5, long-term 5+)
Confidence level (high/medium/low with rationale)
Key beneficiaries (specific companies positioned to capture)

Step 11: Headwinds and risks Same structure as drivers but for negative forces:

Cyclical risks (interest rate sensitivity, economic cycle exposure)
Structural risks (technology disruption, demographic shifts, commoditization)
Regulatory risks (pending legislation, enforcement trends, tariffs)
Geopolitical risks (supply chain concentration, trade policy, sanctions)

Phase 6: Regulatory Environment (2-4 pages)

Step 12: Current regulatory framework

web_search("[INDUSTRY] regulations rules federal state 2024 2025")
web_search("[INDUSTRY] regulatory agencies oversight compliance requirements")
web_search("[INDUSTRY] pending legislation proposed rules impact")
web_search("[INDUSTRY] regulatory risk compliance costs")

Document:

Governing bodies (federal agencies, state regulators, SROs)
Key regulations (name, date enacted, core requirements, compliance cost)
Recent enforcement actions (last 12 months, penalties, trends)
Pending legislation / proposed rules (bill name, status, expected impact, probability)
Regulatory trend direction (tightening, loosening, stable — with evidence)

Step 13: Cross-border regulatory considerations For global industries: compare US, EU, China, and other key market regulatory stances. Identify regulatory arbitrage opportunities and risks.

Phase 7: Financial Benchmarking (3-5 pages)

Step 14: Industry financial profile Pull EDGAR data for top 10 public players and compute:

Metric	Industry Median	Top Quartile	Bottom Quartile	5Y Trend
Revenue Growth	X%	X%	X%	→/↑/↓
Gross Margin	X%	X%	X%	→/↑/↓
EBITDA Margin	X%	X%	X%	→/↑/↓
Net Margin	X%	X%	X%	→/↑/↓
ROIC	X%	X%	X%	→/↑/↓
Capex/Revenue	X%	X%	X%	→/↑/↓
Net Debt/EBITDA	X.Xx	X.Xx	X.Xx	→/↑/↓
FCF Yield	X%	X%	X%	→/↑/↓

Step 15: Valuation context Current sector valuation vs. historical range:

EV/EBITDA: current vs. 5Y avg vs. 10Y avg
P/E: current vs. 5Y avg vs. 10Y avg
EV/Revenue: current vs. 5Y avg vs. 10Y avg
Where multiples sit in historical percentile (e.g., "85th percentile of 10Y range")

Phase 8: Investment Implications (2-3 pages)

Step 16: Synthesize into actionable views

web_search("[INDUSTRY] best stocks investment picks 2025")
web_search("[INDUSTRY] ETFs sector funds")
web_search("[INDUSTRY] analyst ratings sector outlook")

Produce:

Sector call: Overweight / Market Weight / Underweight with 12-month view
Top picks: 3-5 companies best positioned, with 1-line rationale each
Avoid list: 2-3 companies most at risk, with rationale
ETF/index vehicles: relevant sector ETFs with AUM, expense ratio, top holdings
Catalyst calendar: Key events in next 6 months (earnings, regulatory, macro)

Output Format

📊 INDUSTRY DEEP DIVE — [INDUSTRY NAME]
Prepared: [Date] | Geography: [Scope] | Forecast Horizon: [X] Years

━━━ EXECUTIVE SUMMARY ━━━
[5-7 bullet points capturing: market size, growth rate, market structure, key drivers, key risks, investment stance]

━━━ INDUSTRY DEFINITION ━━━
[GICS/SIC/NAICS codes, scope in/out, lifecycle stage, adjacent sectors]

━━━ MARKET SIZING ━━━
Global TAM: $[X]B ([Year]) → $[X]B ([Forecast Year]) | CAGR: [X]%
[Segment breakdown table]
[Geographic breakdown table]
[Top-down vs bottom-up reconciliation]
Growth decomposition: Volume [X]% + Pricing [X]% + Mix [X]% + New Markets [X]%

━━━ COMPETITIVE LANDSCAPE ━━━
Market Structure: [Type] | CR4: [X]% | HHI: [X]
[Market share table — Top 10 with 3-year trend]
[Porter's Five Forces table with ratings and evidence]
[2x2 competitive positioning map]

━━━ VALUE CHAIN ━━━
[Visual flow diagram with margin profiles and key players at each stage]
[Value migration analysis with direction arrows and quantification]

━━━ GROWTH DRIVERS & HEADWINDS ━━━
[Drivers table: description, impact, timeline, confidence, beneficiaries]
[Headwinds table: same structure]
Net assessment: [X] drivers outweigh [X] headwinds — net [positive/negative/neutral]

━━━ REGULATORY ENVIRONMENT ━━━
[Governing bodies and key regulations table]
[Pending legislation tracker with probability and impact]
[Cross-border regulatory comparison (if global)]

━━━ FINANCIAL BENCHMARKING ━━━
[Industry median financial profile table with quartiles and trends]
[Valuation context: current vs. historical ranges]

━━━ INVESTMENT IMPLICATIONS ━━━
Sector Call: [OW/MW/UW] | Conviction: [High/Medium/Low]
[Top picks with rationale]
[Avoid list with rationale]
[ETF/Index vehicles]
[Catalyst calendar — next 6 months]

━━━ TRIPLE-THREAT LENS ━━━
🏦 **Banker:** [M&A trends in the industry — recent deals, likely targets, consolidation drivers, typical deal multiples, strategic vs. financial buyer interest. What would an industry map look like for a sell-side advisor?]

📊 **Accountant:** [Key accounting considerations — revenue recognition standards, capitalization policies, goodwill impairment risk from prior M&A, lease treatment, segment reporting compliance, common audit issues in this sector]

💰 **Wealth Manager:** [Portfolio allocation implications — sector weighting vs. benchmarks, dividend yield characteristics, volatility profile, correlation to broad market, tax-efficiency of sector vehicles, appropriate for which client risk profiles]

━━━ APPENDIX ━━━
A. Data source citations
B. Methodology notes
C. Company-level detail tables
D. Glossary of industry-specific terms

Data Sourcing Fallback Protocol

When primary data sources (EDGAR XBRL API, FRED API, Census API, Massive.com API) are unavailable due to rate limits, downtime, or access issues, use these fallback sources in order. Always disclose which fallback was used and its limitations.

Tier 1 Fallbacks (High Reliability — Government/Institutional Sources)

Primary Source	Fallback Source	URL Pattern	Limitation
EDGAR XBRL API	SEC EDGAR full-text search	`web_search("site:sec.gov [COMPANY] 10-K annual report [YEAR]")`	Must manually extract financials from filing text; slower but same underlying data
FRED API	FRED website direct	`web_fetch("https://fred.stlouisfed.org/series/[SERIES_ID]")`	HTML parsing; data is identical but extraction is less structured
Census Bureau API	Census Bureau data tables	`web_search("site:census.gov [NAICS CODE] industry statistics")`	May require navigating to specific data tables; same underlying data
BLS API	BLS news releases	`web_search("site:bls.gov [INDUSTRY] employment wages")`	Aggregated data; less granular than API queries

Tier 2 Fallbacks (Medium Reliability — Commercial/Research Sources)

Data Need	Fallback Source	How to Access	Reliability	Limitation
Market sizing	IBISWorld	`web_search("IBISWorld [INDUSTRY] market size revenue [YEAR]")`	⭐⭐⭐⭐	Paywall; only summary data available via search. Cite as "IBISWorld estimates"
Market sizing	Statista	`web_search("Statista [INDUSTRY] market size [YEAR]")`	⭐⭐⭐	Aggregates from multiple sources; verify methodology. Cite specific underlying source when visible
Market sizing	Grand View Research / Mordor Intelligence	`web_search("[INDUSTRY] market size CAGR forecast [YEAR]")`	⭐⭐⭐	Tend toward optimistic TAM estimates; use as upper bound
Company financials	Macrotrends	`web_fetch("https://www.macrotrends.net/stocks/charts/[TICKER]/[NAME]/revenue")`	⭐⭐⭐⭐	Sources from SEC filings; good fallback for EDGAR API issues
Industry data	Trade associations	`web_search("[INDUSTRY] trade association industry report statistics")`	⭐⭐⭐⭐	Authoritative for their specific sector; may have membership bias
Employment/wages	Glassdoor / LinkedIn	`web_search("[INDUSTRY] employment trends hiring data [YEAR]")`	⭐⭐	Self-reported; use only for directional trends, not precise figures

Tier 3 Fallbacks (Lower Reliability — Use with Explicit Caveats)

Data Need	Fallback Source	Caveat to Include
Market sizing	Press releases citing research	"Per [Company] press release citing [Research Firm]; primary source not independently verified"
Competitive landscape	News articles	"Based on news reporting; market share figures not independently verified against company filings"
Growth projections	Analyst reports (via search)	"Consensus analyst estimate per [Source]; individual analyst methodologies vary"
Private company data	Crunchbase / PitchBook summaries	"Private company data from [Source]; revenue figures are estimates and may not be audited"

Disclosure Template

When using fallback sources, add this to the Appendix:

━━━ DATA SOURCE NOTES ━━━
⚠️ The following data points used fallback sources due to primary API unavailability:

| Data Point | Primary Source (Unavailable) | Fallback Used | Confidence Impact |
|------------|---------------------------|---------------|-------------------|
| [Market size] | [FRED API] | [Statista via web_search] | Reduced from High to Medium |
| [Company revenue] | [EDGAR XBRL] | [Macrotrends] | Minimal — same underlying SEC data |

All other data points sourced from primary APIs as specified in the Data Sourcing Mandate.

Quality Gates

Professional Standards

A-grade: Market sizing shows methodology, not just a number. Porter's Forces cites specific companies and dollar figures. Value chain margins validated against actual financials. Growth drivers have quantified TAM impact. Regulatory section tracks specific bills by name and status. Investment implications are contrarian where warranted, not just consensus restated.

B-grade: Market size is a single number from one source. Porter's Forces reads like a textbook (generic barriers, unquantified). Value chain is described but not quantified. Growth drivers are a qualitative wish list. No pending legislation detail. Investment section restates obvious consensus views.

Common pitfalls:

Confusing TAM with SAM — overestimating addressable market by 3-5x
Using stale market research (>2 years old) without flagging vintage
Porter's Five Forces as a checkbox exercise instead of genuine strategic analysis
Ignoring private companies in market share analysis (often 30-50% of industry)
Treating regulatory environment as static — missing pending changes that shift the landscape
Providing financial benchmarks without adjusting for company size (comparing mega-cap margins to small-cap)

industry-deep-dive

About

industry-deep-dive

Trigger

Inputs

Dependencies

⚠️ DATA SOURCING MANDATE (NON-NEGOTIABLE)

Methodology

Phase 1: Industry Definition & Scoping (2-3 pages)

Phase 2: Market Sizing (3-5 pages)

Phase 3: Competitive Landscape (5-8 pages)

Phase 4: Value Chain Mapping (3-5 pages)

Phase 5: Growth Drivers & Headwinds (3-5 pages)

Phase 6: Regulatory Environment (2-4 pages)

Phase 7: Financial Benchmarking (3-5 pages)

Phase 8: Investment Implications (2-3 pages)

Output Format

Data Sourcing Fallback Protocol

Tier 1 Fallbacks (High Reliability — Government/Institutional Sources)

Tier 2 Fallbacks (Medium Reliability — Commercial/Research Sources)

Tier 3 Fallbacks (Lower Reliability — Use with Explicit Caveats)

Disclosure Template

Quality Gates

Professional Standards

See Also

Use it

Manifest

Reviews