SKILLEMALL.ai

CC ultimate-seo-geo-skill

Use this skill when running SEO audits, optimizing for AI search engines (AI Overviews, ChatGPT, Perplexity), generating schema markup, diagnosing traffic drops, fixing Core Web Vitals, managing site migrations, or building keyword strategies. Runs scored full-site audits (0-100 Health Score) across 21 modules including technical SEO, E-E-A-T, hreflang, and programmatic SEO. Three modes: Audit, Plan, Execute. NOT for PPC/Ads, social media management, or generic marketing strategy unrelated to organic search.

curiositech/some_claude_skills Hermes author: curiositech 2 files body ≈ 1 062 tokens Open the sourcegithub.com↗ analyzed 39 min ago

Use this skill when running SEO audits, optimizing for AI search engines (AI Overviews, ChatGPT, Perplexity), generating schema markup, diagnosing traffic…

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerShopifyAI and agentstype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
95
Quality 40%
78
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

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.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
For the model run — optional
  • 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-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Bash WebSearch

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 513 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)

Process rating: all ten parameters 60/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 4 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 43 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1062 tokens

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 513: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 43 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.