AC car-maintenance-timeline
Use when the user asks what car maintenance is due or overdue, whether a service can wait, what a dealer 'recommended service' actually contains, or to build a maintenance schedule and budget from mileage, vehicle age, and service history. Computes dual-interval status (every N km OR M months, whichever first), applies a severe-service multiplier for city/short-trip/towing driving, projects a 24-month service timeline from annual mileage, and shows typical cost ranges and DIY difficulty so owners can challenge upselling.
Use when the user asks what car maintenance is due or overdue, whether a service can wait, what a dealer 'recommended service' actually contains, or to build…
As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, 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: 7. 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 56/100
- 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
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 30 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 2408 tokens
- 100Running it twice. No mutating operations
- low 10 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
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 526: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (5 code blocks)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.