analyzing-stranded-asset-risk
Evaluates stranded asset exposure for fossil fuel and carbon-intensive investments with transition modeling. Use when analyzing stranded assets, evaluating fossil fuel exposure, or modeling transition risk.
Best use case
analyzing-stranded-asset-risk is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evaluates stranded asset exposure for fossil fuel and carbon-intensive investments with transition modeling. Use when analyzing stranded assets, evaluating fossil fuel exposure, or modeling transition risk.
Teams using analyzing-stranded-asset-risk 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/analyzing-stranded-asset-risk/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-stranded-asset-risk Compares
| Feature / Agent | analyzing-stranded-asset-risk | 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?
Evaluates stranded asset exposure for fossil fuel and carbon-intensive investments with transition modeling. Use when analyzing stranded assets, evaluating fossil fuel exposure, or modeling transition risk.
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
# Analyzing Stranded Asset Risk Evaluates stranded asset exposure for fossil fuel and carbon-intensive investments with transition modeling. ## When To Use - Assessing portfolio exposure to fossil fuel reserves that may become uneconomic under climate policy or market transition scenarios - Evaluating capital expenditure plans for carbon-intensive assets against decarbonization timelines - Modeling write-down risk for upstream oil & gas, thermal coal, or high-emission infrastructure holdings - Stress-testing investment theses against IEA Net Zero, NGFS, or custom transition pathways - Supporting TCFD-aligned reporting on transition risk and asset impairment ## Inputs To Gather - **Asset or portfolio data**: reserve estimates (proved/probable/possible), production profiles, remaining useful life, book value vs. fair value, and capital committed - **Commodity price assumptions**: current forward curves plus scenario-based price decks (e.g., IEA STEPS, APS, NZE by 2050) - **Policy and regulatory landscape**: carbon pricing (current and projected), phase-out timelines, subsidy removal schedules [VERIFY jurisdiction-specific carbon tax rates and cap-and-trade parameters] - **Technology substitution curves**: renewable levelized cost trajectories, electrification rates, CCUS cost assumptions - **Transition scenario selection**: NGFS orderly/disorderly/hot-house, IEA scenarios, or bespoke client scenarios - **Discount rate and time horizon**: typically 10-30 year horizon; clarify whether using WACC, social cost of carbon, or risk-adjusted rate - **Counterparty/operator data**: breakeven costs per barrel/ton, hedging positions, decommissioning liabilities ## Workflow 1. **Define scope and materiality threshold** - Identify which asset classes are in scope (upstream reserves, midstream infrastructure, power generation, heavy industry) - Set a materiality threshold (e.g., assets representing >1% of portfolio NAV or >X Mt CO2e) - Confirm the transition scenarios to model (minimum two: a base/reference case and at least one accelerated-transition case) 2. **Build the asset-level exposure profile** - Map each asset to its carbon intensity (Scope 1+2 at minimum; Scope 3 for fossil fuel reserves) - Calculate the unburnable carbon fraction: compare reserve volumes against carbon budgets under each scenario - Estimate breakeven prices and compare against scenario price decks — assets with breakeven above scenario price are flagged as stranding candidates 3. **Model transition pathways and impairment triggers** - For each scenario, project revenue streams using scenario-specific demand and price curves - Apply policy cost overlays: carbon tax escalation, emissions trading costs, regulatory compliance capex - Identify the stranding year — the point at which operating costs plus carbon costs exceed revenue under each scenario - Quantify potential write-down as the difference between current book value and discounted cash flows under the stranded scenario 4. **Assess portfolio-level impact** - Aggregate asset-level stranding losses to portfolio level - Calculate stranded asset ratio: (value at risk from stranding) / (total portfolio value) - Segment results by geography, commodity, and time horizon (near-term 2030, mid-term 2040, long-term 2050) - Compare against peer portfolios or benchmark indices where data is available 5. **Evaluate mitigation options** - Identify assets with credible transition plans (fuel switching, CCUS retrofits, repurposing) - Assess divestment timing vs. managed decline economics - Flag assets where decommissioning liabilities may exceed residual value - Note hedging or insurance instruments that offset near-term stranding risk 6. **Compile findings and recommendations** - Summarize stranding exposure by scenario in a comparison table - Highlight the highest-risk positions and recommended actions (divest, hedge, engage, hold-and-monitor) - Note key assumptions and sensitivity drivers (carbon price sensitivity, demand elasticity, technology cost learning rates) ## Output The analysis report should contain: - **Executive summary**: headline stranding exposure figures across scenarios, top 5 at-risk positions - **Scenario comparison table**: rows = asset/sector groupings, columns = scenarios, cells = estimated impairment ($ and % of book value) - **Asset-level detail cards**: for each material position — breakeven price, stranding year by scenario, mitigation feasibility rating (high/medium/low) - **Sensitivity analysis**: tornado chart or table showing which input assumptions most affect total stranding exposure - **Recommendation matrix**: action (divest/hedge/engage/monitor) mapped to each material position with rationale - **Methodology appendix**: scenario sources, carbon budget assumptions, discount rates, data vintage ## Quality Checks - Confirm carbon budget figures align with cited IPCC/IEA report vintage — do not mix AR5 and AR6 budgets [VERIFY which IPCC assessment cycle the client references] - Validate breakeven cost data against recent operator filings or industry databases (Rystad, Wood Mackenzie) - Ensure scenario price decks are internally consistent (e.g., NZE demand destruction should match NZE price levels) - Check that discount rates are appropriate for the asset type and do not inadvertently minimize long-dated stranding risk - Verify decommissioning liability estimates against regulatory requirements in each operating jurisdiction [VERIFY local decommissioning bond/fund requirements] - Cross-check Scope 3 emissions factors against recognized databases (EXIOBASE, EPA emission factors) [VERIFY emission factor vintage] - Confirm that the analysis does not constitute investment advice unless appropriately disclaimed — include standard disclaimer language for the output jurisdiction