AB tailor-cover-letter
Tailor and polish an existing cover letter in Google Docs for a specific role while keeping every claim consistent with the resume that will accompany it. Use when the user provides an editable Google Docs cover-letter link, the associated resume, and a role description as either pasted text or a direct link, and wants spelling, grammar, punctuation, style, clarity, persuasive focus, or organization improved without invented experience. Apply safe copyedits directly, but propose and obtain approval before adding topics, personal experiences, claims, or other substantive content.
Tailor and polish an existing cover letter in Google Docs for a specific role while keeping every claim consistent with the resume that will accompany it.
As a process B 74/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting
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 74/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 45 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1756 tokens
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 585: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 45 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.