AC bicycle-maintenance-repair
Use when your bicycle (or e-bike) is your primary gig-delivery vehicle, daily commuter, or emergency transport and shop visits would kill your income or mobility. The agent diagnoses symptoms from your descriptions/photos/text, builds a personalized maintenance calendar based on mileage/conditions, sources exact parts with links and prices, tracks inventory and repair history via filesystem, generates checklists and reminder sequences, and escalates to professional help only when unsafe. You perform the physical repairs, test rides, cleaning, and adjustments using hand tools. Zero shop dependency, maximum uptime in the post-AI gig economy.
As a process C 63/100 · 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: 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 63/100
- 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. 9 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 65Failures and branches. 3 branches
- 100Tools and files. No external tools needed
- 100Steps. 77 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2196 tokens
- 100Progress reporting. Reports progress
- 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
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 647: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 77 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.