BD financial-statement-normalization
Converts reported financial statements into valuation-ready numbers — capitalizes R&D into a research asset, capitalizes operating leases into debt, strips one-time items, normalizes cyclical or trough earnings, derives FCFF and FCFE, and computes invested capital, ROIC, economic value added and the diagnostic ratio pack. Use when cleaning up EBIT before a DCF, capitalizing research or leases, deciding whether a restructuring charge is really non-recurring, computing free cash flow to the firm or to equity, measuring return on invested capital, normalizing earnings for a cyclical or commodity company, or building the base-year operating income and capital base that a valuation model consumes.
Converts reported financial statements into valuation-ready numbers — capitalizes R&D into a research asset, capitalizes operating leases into debt, strips…
As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
- warning
body-longSKILL.md body ≈ 6323 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 196): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 42/100
- 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. 18 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Execution cost. Instruction body is 6323 tokens
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- low 12 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 701: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.