BC monte-carlo-valuation
Turns a point-estimate valuation into a distribution — samples the drivers you are least sure about from normal, lognormal, triangular, uniform or discrete-scenario distributions, re-runs the DCF engine on every draw, and reports percentiles, mean, standard deviation, the probability the value exceeds the market price, and the share of draws the engine refused as infeasible. Correlates drivers through a shared common factor. Use when running a Monte Carlo simulation or a probabilistic valuation, putting a range or confidence band around a value per share, asking how likely it is that a stock is under- or overvalued, valuing a commodity or cyclical company where one macro variable such as the oil price dominates, or turning a scenario grid into a probability-weighted expected value.
Turns a point-estimate valuation into a distribution — samples the drivers you are least sure about from normal, lognormal, triangular, uniform or…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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: 6. 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
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4098 tokens
- 100Steps. 14 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 11 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 792: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.