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AB geo-metrics-tracker

Real-time GEO metrics monitoring and alerting orchestrator. Use this skill whenever the user wants to track, visualize, and react to AI GEO performance metrics over time — especially AIGVR (AI-generated visibility rate), SoM (Share of Model), AI citation volume, and related indicators across ChatGPT, Perplexity, Gemini, Claude, SGE, and other generative engines. Prefer this skill whenever the request goes beyond a static report and instead focuses on ongoing dashboards, time-series tracking, anomaly detection (sudden spikes/drops), or building an internal “GEO metrics command center.” It is designed to complement `geo-report-builder` by turning one-off analyses into a continuous monitoring system.

ClawHub Agent Skills author: GEOlyAI v0.1.0 MIT-0 5 files body ≈ 3 740 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 74/100 · Nearly there — weak spots: inputs and preconditions, running it twice

AnalyzerData and analyticstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
B
74/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
60
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: geo-metrics-tracker (ClawHub)

How to improve

    For the model run — optional
    • 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: 4. 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 74/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 10 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 8 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 151 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3740 tokens
    • 100Progress reporting. Reports progress
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 706: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 151 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: clean
    This is a planning and template skill for GEO metrics dashboards and alerts, with no evidence of hidden data access or autonomous monitoring behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026