AC beyond-seo
Use for SEO audits, AEO/GEO strategy, local SEO, backlink review, competitor research, keyword mapping, Apify/native scraping workflows, and client-ready SEO reporting.
Use for SEO audits, AEO/GEO strategy, local SEO, backlink review, competitor research, keyword mapping, Apify/native scraping workflows, and client-ready SEO…
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 80. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7241 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 54/100
- 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
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (beyond-seo) differs from the folder (beyond-seo-geo)
- 50When it triggers. No condition that starts the skill
- 70Execution cost. Instruction body is 7241 tokens
- 85Steps. 482 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 26 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 168: enough signal without eating the budget
- +4Structure: 61 headings
- +3Step-by-step instructions: 482 items
- +4Has examples (31 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.