conducting-industry-credit-analysis
Structures industry-level credit assessment with cyclicality analysis, regulatory risk, and sector-specific credit metrics. Use when analyzing industry credit conditions, evaluating sector risk, or building industry-level views.
Best use case
conducting-industry-credit-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures industry-level credit assessment with cyclicality analysis, regulatory risk, and sector-specific credit metrics. Use when analyzing industry credit conditions, evaluating sector risk, or building industry-level views.
Teams using conducting-industry-credit-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/conducting-industry-credit-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How conducting-industry-credit-analysis Compares
| Feature / Agent | conducting-industry-credit-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures industry-level credit assessment with cyclicality analysis, regulatory risk, and sector-specific credit metrics. Use when analyzing industry credit conditions, evaluating sector risk, or building industry-level views.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Conducting Industry Credit Analysis Structures industry-level credit assessment with cyclicality analysis, regulatory risk, and sector-specific credit metrics for credit markets, leveraged lending, and direct lending portfolios. ## When To Use - Building or updating an industry credit view for portfolio allocation or sector concentration limits - Evaluating whether to enter, increase, or reduce exposure to a specific industry vertical - Underwriting a new leveraged loan or direct lending deal and needing sector context - Stress-testing portfolio industry concentrations against cyclical or regulatory scenarios - Preparing industry risk commentary for investment committee or credit committee memos ## Inputs To Gather - **Industry classification**: GICS sub-industry, NAICS code, or internal sector taxonomy for the target industry - **Credit universe data**: Default rates, recovery rates, and ratings migration history for the sector (Moody's, S&P, or internal data) - **Financial benchmarks**: Sector-level leverage multiples (Debt/EBITDA), interest coverage ratios, free cash flow conversion, and margin profiles - **Cyclicality indicators**: Revenue volatility, correlation to GDP/industrial production, and historical peak-to-trough EBITDA declines - **Regulatory landscape**: Key regulatory bodies, pending or recent rule changes, and compliance cost burden [VERIFY jurisdiction-specific regulatory bodies and recent legislative changes] - **Competitive dynamics**: Market concentration (HHI or CR-4), barriers to entry, pricing power, and disruptive technology exposure - **Capital structure norms**: Typical leverage levels, secured vs. unsecured mix, and covenant structures prevalent in the sector ## Workflow 1. **Define scope and classification** - Confirm industry boundary (narrow sub-industry vs. broad sector) and ensure consistent classification across data sources - Identify the relevant credit cycle phase: expansion, peak, contraction, or trough 2. **Assess structural credit characteristics** - Evaluate revenue visibility (contracted vs. spot, recurring vs. project-based) - Measure operating leverage — ratio of fixed to variable costs and its impact on EBITDA volatility - Benchmark sector median leverage, coverage, and FCF metrics against the broader credit universe - Determine asset tangibility and collateral quality typical for the industry 3. **Analyze cyclicality and stress scenarios** - Quantify historical peak-to-trough EBITDA decline using at least two prior downturns - Map the industry's sensitivity to macro variables (interest rates, commodity prices, consumer spending, capex cycles) - Run a base, stress, and severe-stress scenario on key credit metrics (leverage, coverage, liquidity) - Flag industries where stress-case leverage exceeds 6.0x or coverage falls below 1.0x as elevated risk 4. **Evaluate regulatory and ESG risk** - Identify primary regulatory frameworks and agencies with jurisdiction [VERIFY applicable regulatory bodies per geography] - Assess pending regulation that could materially alter cost structures, revenue models, or market access - Note ESG-related transition risks (carbon exposure, stranded asset potential, labor practices scrutiny) 5. **Benchmark default and recovery experience** - Pull historical default rates by rating category within the industry - Analyze recovery rates by seniority (1st lien, 2nd lien, unsecured) and compare to all-industry averages - Identify whether the sector has exhibited higher-than-average loss-given-default due to asset specificity or distressed-sale dynamics 6. **Synthesize industry credit opinion** - Assign a qualitative industry risk tier (favorable, neutral, cautious, negative) - Articulate the two or three key credit drivers and primary risk factors - Define recommended underwriting guardrails: maximum leverage, minimum coverage, structural protections (covenants, asset pledges) ## Output Produce an **Industry Credit Assessment Memo** containing: - **Industry overview**: Classification, size, growth trajectory, and competitive structure - **Credit metrics dashboard**: Table of sector median Debt/EBITDA, interest coverage, FCF/debt, default rate, and recovery rate — with historical range and current positioning - **Cyclicality profile**: Sensitivity mapping, peak-to-trough analysis, and stress scenario results - **Regulatory and ESG risk summary**: Key regulatory exposures and pending changes with materiality assessment - **Credit opinion**: Risk tier, key credit drivers, primary risks, and recommended underwriting parameters - **Watchlist items**: Specific trends, pending regulations, or structural shifts requiring ongoing monitoring ## Quality Checks - All credit metrics are sourced and time-stamped; no unsourced benchmarks presented as fact - Cyclicality analysis includes at least two historical stress periods with quantified EBITDA impact - Regulatory risk section cites specific statutes, agencies, or pending rules — not vague references to "regulatory risk" [VERIFY all cited regulations are current] - Default and recovery statistics specify the data provider, time horizon, and sample size - Stress scenarios explicitly state assumptions (GDP decline, rate change, commodity move) rather than generic "adverse conditions" - Industry risk tier is supported by the preceding quantitative and qualitative analysis, not asserted without evidence - Output distinguishes between confirmed data and analyst judgment — assumptions marked with [VERIFY] where data is estimated or extrapolated