BC dcf-valuation-engine
Runs an intrinsic (discounted cash flow) valuation from value drivers — builds the year-by-year forecast with fading growth, margins and reinvestment, closes it with a terminal value, adjusts for failure risk, walks the bridge from operating assets to equity value per share, and runs sensitivity grids. Also reverse-engineers the market price into the growth or margin it already assumes. Use when valuing a company intrinsically, building a DCF or FCFF/FCFE model, computing terminal value or value per share, running valuation sensitivity, or asking what the market is pricing in.
Runs an intrinsic (discounted cash flow) valuation from value drivers — builds the year-by-year forecast with fading growth, margins and reinvestment, closes…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2206 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 583: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.