industry-sector-analysis

name	industry-sector-analysis
version	1.5.1
last_modified	2026-03-22
description	Use when assessing industry dynamics, cyclicality, sector-specific KPIs, competitive structure, or sector-adjusted credit risk before detailed issuer modeling. This skill frames what metrics matter, what a sector can support through the cycle, and which structural or documentation issues deserve extra scrutiny.
category	sector-analysis
related_skills	["credit-memo-generator","credit-modeling-and-valuation","credit-surveillance-monitoring","debt-structure-covenants","due-diligence-and-assessment","portfolio-investment-process","trading-pricing-mechanics"]
triggers	["industry analysis","sector analysis","sector risk","industry dynamics","sector benchmarks","cyclical credit","defensive credit","industry-specific metrics","sector-specific red flags"]
disambiguation	Use this skill when the question is primarily about industry structure, sector behavior, peer context, or what operating metrics matter most. Use credit-modeling-and-valuation for issuer-specific forecasting, ratio analysis, and scenario mechanics. Use debt-structure-covenants for documentation analysis when the core issue is baskets, liens, leakage, or amendment risk. Use trading-pricing-mechanics for current spreads, market levels, or secondary pricing context. Use cre-analysis-underwriting when the asset is a property, real estate portfolio, or CRE loan rather than a corporate issuer.

Industry & Sector Analysis

Sector analysis provides the context around cyclicality, competitive structure, pricing power, regulation, and sector KPIs that determine what a credit can support through the cycle. Use it to frame the issuer before detailed modeling, documentation review, or surveillance work.

Core Workflow

1. Define the sector and value chain: Identify where the issuer sits in the value chain, which customers and inputs matter most, and whether the business behaves like the headline sector or a narrower sub-segment.
2. Place the sector in cycle context: Separate cyclical pressures from secular change and decide whether current performance reflects peak, mid-cycle, trough, or structural transition conditions.
3. Identify the 3-5 KPIs that actually drive credit quality: Focus on the operating metrics that explain revenue visibility, margins, working capital intensity, capital needs, and refinancing resilience.
4. Translate sector traits into credit implications: Convert cyclicality, pricing power, regulation, customer concentration, and capex intensity into leverage tolerance, downside severity, and likely recovery behavior.
5. Check rating and documentation pressure points: Note how agencies and lenders usually adjust for the sector, including lease treatment, addback risk, backlog quality, reserve assumptions, or regulated returns.
6. Stress the sector-specific risks: Pressure test the few variables that really matter rather than applying only generic top-line and margin shocks.
7. Hand off clear sector context: Pass cycle position, KPI interpretation, peer framing, and key red flags into modeling, covenant review, surveillance, or memo-writing work.

Reference Map

Read the narrowest relevant reference rather than loading the full library.

Default reading order

1. references/cross-asset-sector-framework.md — Start here: the five-dimension universal scaffold for cross-sector and cross-asset comparisons (corporate, private credit, CRE, infrastructure, structured finance).
2. references/sector-toolkit.md — Compact overlays: cyclical and secular framing, pricing power and pass-through, cross-sector comparison shortcuts, leverage calibration prompts, documentation watchpoints, and supply-chain vulnerability.
3. references/technology-disruption-timeline.md — Timing and debt-life lens: disruption horizons, maturity alignment, and refinancing-risk framing across industries. This is not a substitute for references/industry/technology.md, which holds the technology-sector operating detail, KPIs, and sub-sector deep dives. For property-type digital infrastructure, use references/industry/real-estate-data-centers.md for data centers and references/industry/real-estate-alternative.md for towers and related alternative real-estate digital infrastructure.
4. references/industry/*.md — Sector-specific files as needed (for example agribusiness-protein-fertilizer, aerospace-defense, automotive, banks, business-services, building-construction, capital-markets-asset-management, chemicals, consumer-finance, energy-midstream, environmental-services-waste, financial-services, food-beverage, gaming-leisure-hospitality, healthcare, household-personal-care-consumer-products, industrials-manufacturing, insurance, media, metals-mining, packaging, paper-forest-products, payments-fintech, pharma-biotech-medtech, power-generation-renewables, real-estate-alternative, real-estate-data-centers, real-estate-hotels, real-estate-industrial-logistics, real-estate-office, real-estate-residential, real-estate-retail, retail-consumer, specialty-finance-leasing, technology, telecom, tobacco, transportation-logistics, and utilities-infrastructure).

Output Deliverables

When asked to analyze a sector or provide industry context for a credit, produce:

1. Source citations: Explicitly cite all data, qualitative claims, and benchmark inputs.
   • When a stable direct URL exists, include it inline with the citation and keep any page, slide, filing-date, report-date, or access-date detail in the same citation.
2. Sector framing: Market structure, cycle position, and whether the key issue is cyclical, secular, regulatory, or company-specific.
3. Key credit metrics: The sector-specific KPIs that matter most and how to interpret them.
4. Sector-adjusted leverage view: What the sector can generally support, using references/rating-agency-thresholds.md for any current numerical calibration.
5. Agency and structural watchpoints: What agencies and lenders usually focus on for this sector.
6. Common red flags: The operating or structural warning signs that most often precede credit deterioration.
7. Stress implications: The few downside scenarios most likely to break the thesis.
8. Handoff inputs: Clear takeaways for modeling, covenant review, surveillance, or memo writing.

Limitations

• Sector labels can hide more than they reveal; some issuers behave like a niche sub-sector rather than the headline industry.
• Sector heuristics are starting points, not rating outcomes; issuer-specific positioning, financial policy, and documentation still dominate final credit judgment.
• Benchmark ranges are least useful at cyclical peaks, during structural transition, or when accounting treatment materially distorts reported EBITDA.
• For broader framework limitations, consult skills/credit-memo-generator/references/analytical-limitations.md.

Data Quality

• When data is incomplete or unavailable, consult skills/credit-memo-generator/references/incomplete-data-guidance.md for gap disclosure templates. Never silently fill gaps with assumptions.
• If the answer depends on current spread levels, current rating calibration, or current documentation conditions, use the relevant root reference rather than relying on timeless sector notes.
• When applying standard analytical frameworks, consult skills/credit-memo-generator/references/analytical-limitations.md to identify when the framework may produce unreliable results. Disclose limitations proactively rather than presenting framework outputs as authoritative.

Examples

• examples/worked-sector-analysis-example.md: Sector analysis for business services showing market structure, cycle framing, key KPIs, sector risks, and sector-adjusted credit interpretation.