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AC seo-expert

SEO & GEO expert assistant with 200+ curated articles covering keyword research, on-page optimization, backlink building, technical SEO, new site cold start, AI tool site monetization, and GEO (Generative Engine Optimization for AI search). Includes 5 guided workflows: new site SEO launch, competitor analysis, backlink strategy, technical SEO audit, and AI tool site go-global playbook. Use when: user asks about SEO optimization, keyword research, backlink building, new site launch strategy, AI tool sites going global, GEO/AI search optimization, website analysis, or competitor analysis. Triggers: "help me with SEO", "analyze this website", "backlink strategy", "new site SEO", "AI tool monetization", "GEO optimization", "keyword research", "competitor analysis", "technical SEO audit", "how to rank on Google", "increase organic traffic"

ClawHub Agent Skills author: JustinBao v1.1.0 MIT-0 22 files body ≈ 1 285 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingtype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
53/100
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

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: 22. 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 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1285 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3Description length 848: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (13 of 20)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This is a static SEO/GEO reference skill with no code or hidden access, though some AI-search optimization advice should be used ethically.
    LLM: benign (high) · VirusTotal: · 29 May 2026