AC shopify-product-serp-optimizer
Audit and improve Shopify product SERP performance with five-product batches, evidence-backed metadata recommendations, product image Alt Text checks, HTML reports, and one approval bundle for safe writes. Use for product-page search snippet work, not technical SEO, translations, redirects, theme work, or generic content strategy.
Audit and improve Shopify product SERP performance with five-product batches, evidence-backed metadata recommendations, product image Alt Text checks, HTML…
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-requests-env-secretSKILL.md:1Skill asks the runtime to inject credential env vars into its sandbox: SKILL_HUB_SHOPIFY_CLIENT_SECRET, SKILL_HUB_SHOPIFY_APP_AUTOMATION_TOKEN — verify each one is needed for the stated purposerequired_environment_variables: SKILL_HUB_SHOPIFY_CLIENT_SECRET, SKILL_HUB_SHOPIFY_APP_AUTOMATION_TOKEN
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 332 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "required_environment_variables"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 60Consistency. The Hermes dialect needs category and tags
- 100Steps. 36 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Execution cost. Instruction body is 1335 tokens
- 100Running it twice. No mutating operations
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
- +2Single-language instructions
- +3Description length 332: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 36 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.