AC google-autoreply
Onboard a merchant into the Auto Google Review Reply product: register/login, connect a Google Business Profile, benefit from a 5-day free trial (membership check, no forced payment), and configure AI auto-reply. Invoke when the user wants to set up an account, connect Google Business, check membership/ trial status, or enable AI review auto-reply.
Onboard a merchant into the Auto Google Review Reply product: register/login, connect a Google Business Profile, benefit from a 5-day free trial (membership…
As a process C 55/100 · Has gaps — weak spots: result and completion, consistency, running it twice
The same skill appears in 1 more place: ClawHub
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 55/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 22 mutating operations with no state check
- 40Consistency. Frontmatter name (google-autoreply) differs from the folder (google-autoreply-setup)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4251 tokens
- 85Steps. 66 steps, 1 vague phrases
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (11 tags): a typed call is more reliable
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 350: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 66 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.