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AC google-maps-search-api-skill

This skill is designed to help users automatically extract business data from Google Maps search results. The Agent should proactively apply this skill when the user makes the following requests: 1. Search for coffee shops in a specific city; 2. Find dentists or medical clinics nearby; 3. Track competitors' locations in a certain area; 4. Extract business leads from Google Maps lists; 5. Gather restaurant data for market research; 6. Find hotels or accommodation options in a region; 7. Locate specific services like coworking spaces or gyms; 8. Monitor new business openings in a neighborhood; 9. Collect contact information and addresses for sales prospecting; 10. Analyze price ranges and cuisines of local eateries; 11. Get ratings and review counts for a list of businesses; 12. Export local business data into a CRM or database.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 378 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill is designed to help users automatically extract business data from Google Maps search results.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationSales and CRMAI and agentsInfrastructuretype 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%
84
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 50 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1378 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 838: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.