Finance DCF Valuation
SkillCommerce & financeThis skill turns your AI into a careful valuation assistant. It builds a discounted cash flow view with every assumption written out, cross-checks the result against valuation multiples, and adds confidence tiers and margin-of-safety framing so you can see how solid the number is before making a call.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, give your AI the company or figures you want valued and ask for a valuation. Ask it to lay out its assumptions so you can adjust them and see how the result changes.
Then ask your AI: use the Finance DCF Valuation skill
What your AI can do with it
- Run a discounted cash flow valuation with all assumptions stated in the open
- Cross-check the result against valuation multiples
- Assign confidence tiers to the estimates
- Frame a margin of safety around the final valuation
- Present the analysis as a decision-ready view
What this skill tells your AI
The instructions your AI receives, as published by contextgo/contextgo in src/process/resources/skills/finance-analyst-pack/skills/finance-dcf-valuation/SKILL.md and read by ahel’s review.
Use this skill when the user needs a valuation workflow, not just a number.
Use when
- The user asks whether a business or asset appears undervalued, fairly valued, or stretched.
- A DCF or scenario-based fair-value view is needed.
- The task requires explicit assumptions and sensitivity rather than intuition.
Do not use when
- Inputs are too weak for any serious cash-flow view.
- The request is purely accounting analysis with no valuation angle.
- The user expects real-time market data that is not available in the current files.
Core valuation rules
- Start with a data-quality gate.
- State all critical assumptions explicitly.
- Use bull, base, and bear scenarios.
- Cross-check DCF outputs against relative valuation sanity bounds.
- Present ranges, confidence, and gaps instead of pretending a point estimate is truth.
Workflow
1. Run the input quality gate
Check:
- freshness of the periods
- completeness of revenue, margin, cash flow, debt, cash, and share inputs
- consistency of the statement package
If the data is weak, downgrade to directional valuation.
2. Build the valuation logic
At minimum, make explicit:
- forecast horizon
- revenue path
- margin path
- reinvestment and working-capital assumptions
- discount-rate logic
- terminal-value method
3. Scenario the result
Always provide:
- bull
- base
- bear
Each scenario should state what changes and why.
4. Run sensitivity and sanity checks
Sensitivity should cover at least:
- discount rate
- terminal growth or exit assumption
Sanity checks should ask:
- does the implied range make sense against market or peer multiples
- is terminal value dominating too much
- are the assumptions drifting into optimism rather than analysis
5. Conclude with confidence and safety zone
The final answer should say:
- fair-value range
- confidence level
- key gaps that could move the conclusion
- margin-of-safety discipline
Do not issue personalized investment advice.
Output format
Return:
1. Data quality gate
- freshness
- completeness
- confidence tier
2. Scenario assumptions
- bull
- base
- bear
3. Valuation work
- DCF summary
- sanity-check view
- sensitivity summary
4. Fair-value view
- range
- safety zone
- what would change the view materially
Use together with
finance-financial-statement-analysisfinance-ratio-benchmarkingfinance-investment-memo
Signals
- GitHub stars
- 54
- Forks
- 5
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
finance-dcf-valuation- Source
- github.com/contextgo/contextgo