BD payout-policy-analysis
Assesses whether a company returns the right amount of cash to shareholders. Computes FCFE over a history under three debt assumptions, compares it against dividends plus buybacks to give the cash surplus or deficit, places the firm in the dividend matrix of affordability against project quality, projects forward FCFE and buyback capacity, and benchmarks payout ratio and dividend yield against peers and the market. Handles bank FCFE from regulatory capital. Use for dividend policy, payout ratio, dividend yield, potential dividends, FCFE, cash returned, buybacks, share repurchase capacity, excess cash, dividend cut or increase, cash accumulator or overpayer, or the dividend matrix.
Assesses whether a company returns the right amount of cash to shareholders.
As a process D 38/100 · Unfinished process — 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 134, 336): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 38/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Execution cost. Instruction body is 4569 tokens
- 75Steps. 3 steps
- 100Consistency. Name and required fields are in place
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 689: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 3 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.