SKILLEMALL.ai
GitHub ↗ RU Sign in

AB geo-local-optimizer

Local business-focused GEO optimization orchestrator for AI-powered local search. Use this skill whenever the user mentions local shops, clinics, restaurants, service providers, offline stores, franchise locations, or service areas and wants to rank better in AI answers or map-style results for queries like "near me", city/area + service, or landmark-based searches. Always consider this skill when the request combines GEO, local SEO, maps/listings, store pages, reviews, or service areas, even if the user does not explicitly say "GEO" or "local SEO".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 4 213 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Local business-focused GEO optimization orchestrator for AI-powered local search.

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Steps w 15
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-local-optimizer (LeoYeAI/openclaw-master-skills)

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 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Steps. 160 steps, 10 vague phrases
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4213 tokens
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations
    • 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

    • +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
    • +5Description quotes 2 example trigger phrases
    • +3Description length 555: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 160 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.