Discounted cash flow valuation with sensitivity analysis
npx skills add https://github.com/openai/plugins --skill dcf
Build a discounted cash flow (DCF) valuation for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.
Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.
Follow these steps:
Look up the company by ticker using discover_companies. Capture:
company_idlatest_calendar_quarter — anchor for all period calculations below (see ../data-access.md Section 1.5)latest_fiscal_quarter../data-access.md Section 4.5Get market-side inputs for {TICKER} (see ../data-access.md Section 2 for how to source market data in your environment):
If market data is unavailable, use reasonable defaults: beta=1.0, risk-free rate=4.5%, and note the assumptions.
Calculate 8 quarters backward from latest_calendar_quarter. Pull:
Also pull segment revenue and any available guidance series.
Cost of Equity (CAPM):
Cost of Debt:
Capital Structure:
Show all inputs and the resulting WACC clearly.
Before projecting top-down, attempt a bottoms-up revenue build using operational KPIs. This produces a significantly more defensible DCF — a top-down trend decay is a guess; a bottoms-up KPI build is analysis.
Discover segment and KPI data:
Pull segment revenue breakdown + segment-specific KPIs for the target company. Use the sector taxonomy to know what to search for:
Build bottoms-up projections per segment:
For each segment with KPI data, project revenue using unit economics:
Sum segment projections to get total revenue for each of 5 years. Show the build clearly so the reader can challenge individual segment assumptions.
Fall back to top-down if KPIs aren't available. If segment KPIs are sparse or unavailable, use the top-down approach in Section 5b instead, but note explicitly that the model is less reliable without bottoms-up drivers.
Build 5-year FCF projections. If a projection engine is available (see ../data-access.md Section 5), use it. Otherwise, project manually:
Show all assumptions clearly — this is the most judgment-intensive part. If using this fallback instead of the KPI-driven build (Section 5a), note the limitation.
Calculate terminal value using perpetuity growth method:
Also compute:
Build a sensitivity table varying two key inputs:
WACC (rows): Base WACC ± 2% in 0.5% increments (7 rows)
Terminal Growth Rate (columns): 1.5% to 4.0% in 0.5% increments (6 columns)
Each cell = implied share price at that WACC/growth combination.
Highlight the base case cell and the current market price for reference.
Also show a secondary sensitivity: Revenue Growth vs FCF Margin if data supports it.
If consensus estimates are available (see ../data-access.md Section 3):
If consensus data is not available, skip this check.
Flag any issues:
Challenge your own assumptions — don't anchor to the current price:
Save to reports/{TICKER}_dcf.html using the HTML report template from ../design-system.md. Write the full analysis as styled HTML with the design system CSS inlined. This is the final deliverable — no intermediate markdown step needed.
Structure the report with these sections:
<h1>{Company Name} ({TICKER}) — DCF Valuation</h1>
<p>Generated: {date}</p>
<h2>Summary</h2>
<table>
| Metric | Value |
| Current Price | $XXX |
| Implied Share Price | $XXX |
| Upside / Downside | +X.X% / -X.X% |
| WACC | X.X% |
| Terminal Growth | X.X% |
| Terminal Value % of Total | XX% |
</table>
<h2>WACC Calculation</h2>
<table>
| Component | Value | Source |
| Risk-Free Rate | X.X% | FRED 10Y Treasury |
| Equity Risk Premium | 5.5% | Standard assumption |
| Beta | X.XX | Market data |
| Cost of Equity | X.X% | CAPM |
| Cost of Debt (after-tax) | X.X% | Interest/Debt × (1-t) |
| Equity Weight | XX% | Market cap |
| Debt Weight | XX% | Total debt |
| **WACC** | **X.X%** | |
</table>
<h2>Historical Free Cash Flow (8 Quarters)</h2>
<table>
| Metric | Q1 | Q2 | ... | Q8 |
{OCF, CapEx, FCF, FCF Margin — with Daloopa citations}
</table>
<h2>FCF Projections (5 Years)</h2>
<table>
| Metric | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
{Revenue, FCF Margin, FCF — with assumptions noted}
</table>
<h2>Valuation Bridge</h2>
<table>
| Component | Value |
| PV of Projected FCFs | $XXX |
| PV of Terminal Value | $XXX |
| Enterprise Value | $XXX |
| Less: Net Debt | ($XXX) |
| Equity Value | $XXX |
| Shares Outstanding | XXX |
| **Implied Share Price** | **$XXX** |
</table>
<h2>Sensitivity Table: WACC vs Terminal Growth</h2>
<table>
| WACC \ Growth | 1.5% | 2.0% | 2.5% | 3.0% | 3.5% | 4.0% |
{matrix of implied share prices, base case bolded}
</table>
<p>Current market price: $XXX for reference.</p>
<h2>Key Assumptions & Risks</h2>
<ul>{List all key assumptions and what could invalidate them}</ul>
<h2>Sanity Checks</h2>
<ul>{Implied multiples vs historical, terminal value concentration, etc.}</ul>
All financial figures must use Daloopa citation format: <a href="https://daloopa.com/src/{fundamental_id}">$X.XX million</a>
Tell the user where the HTML report was saved.
Summarize: implied price vs current price, key upside/downside drivers, and the biggest sensitivity.
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Take openai/dcf from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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