AC corporate-credit-memo
Generates institutional-grade corporate credit application memoranda (Credit Memos) in English from uploaded annual reports, financial statements, or user-provided financial data. Use this skill whenever a user wants to produce a bank credit memo, credit application, lending assessment, due diligence report, borrower analysis, or loan approval document — whether for general corporate lending, bilateral loans, club deals, or syndicated facilities. Also triggers for requests like "analyse this company for a loan", "write up a credit paper", "prepare a credit committee memo", "assess this borrower", or "draft a credit application for [company name]". Designed for banking professionals including credit analysts, relationship managers, risk officers, and CROs. Applies UK/international banking standards with awareness of PRA supervisory expectations.
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 85Steps. 45 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1834 tokens
- 100Progress reporting. Reports progress
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 856: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +4Structure: 14 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.