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AB geo-prompt-researcher

Discover high-value AI search prompts your target audience uses on ChatGPT, Perplexity, Gemini, and Claude. Research and generate comprehensive prompt lists for GEO (Generative Engine Optimization) strategy, including discovery, comparison, how-to, definition, and recommendation prompts. Use whenever the user mentions researching AI search queries, finding GEO prompts, building prompt monitoring lists, understanding what people ask AI about their brand/category, or wants to identify AI search opportunities for their product/service.

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 1 536 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Discover high-value AI search prompts your target audience uses on ChatGPT, Perplexity, Gemini, and Claude.

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, running it twice

GeneratorAsanaNotionAI and agentstype 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%
94
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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: 6. 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 65/100

    • 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
    • 30Running it twice. 2 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
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1536 tokens
    • medium 5 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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 538: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.