SKILLEMALL.ai
GitHub ↗ RU Sign in

AB seo-audit

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 1 file body ≈ 2 014 tokens Open the sourcegithub.com↗ analyzed 2 d ago

When the user wants to audit, review, or diagnose SEO issues on their site.

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

AnalyzerMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: seo-audit (LeoYeAI/openclaw-master-skills)

The same skill appears in 2 more places: awesome-openclaw-skills, RA-Skills

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 1. 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 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 210 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2014 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 350: enough signal without eating the budget
    • +4Structure: 33 headings
    • +3Step-by-step instructions: 210 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.