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AB search-console-report

Generate comprehensive SEO analysis reports from Google Search Console data with PDF export. Use when the user wants to analyze search performance, get SEO insights, view traffic trends, top pages, top keywords, country/device distribution, or generate a professional PDF report for one or more websites using Google Search Console API. Requires a Google Cloud Service Account JSON key with Search Console read access.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 445 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate comprehensive SEO analysis reports from Google Search Console data with PDF export.

As a process B 79/100 · Nearly there — no weak spots found

AnalyzerPDFGoogle CloudData and analyticsMarketingtype 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
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
B
79/100
Nearly there
Tools and files w 18
60
Result and completion w 14
60
Failures and branches w 10
65
the three weakest of ten parameters · all ten

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-private-key SKILL.md:23
      Private key material (placeholder value)
      "private_key": "-----BEGIN PRIVATE KEY----- …
      placeholder

    Files scanned: 2. 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 79/100

    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4445 tokens
    • 100Steps. 44 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 418: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 44 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.