AB seo-geo-qa
Check blog posts and articles before publishing. Finds broken links, weak sources, missing SEO elements, and citation problems. Use when: reviewing a draft, auditing content quality, checking if links still work, verifying sources are credible, running pre-publish QA, or doing post-publish page checks. Also triggers on: 'check this article', 'verify my links', 'review before publishing', 'content audit', 'source quality check', 'are my links working', 'SEO review', 'pre-publish checklist'. Generates markdown+JSON reports with PASS/FAIL verdict. Python stdlib only, no dependencies.
As a process B 65/100 · 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "displayName"
Process rating: all ten parameters 65/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) 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
- 85Steps. 16 steps, 1 vague phrases
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 951 tokens
- 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
- +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 587: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.