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AB finding-real-estate-professionals-on-twitter

Finds real estate agents, brokers, property investors, and real estate professionals on Twitter/X using apidojo's Twitter User Scraper on Apify. Triggers when the user asks to: find real estate agents on Twitter, discover property professionals on X for outreach, build a list of realtors active on Twitter, find real estate investors or brokers on X, prospect real estate professionals via their Twitter bios, identify mortgage brokers or property managers on Twitter, or compile a real estate professional contact list from Twitter. Returns username, bio, follower count, verification status, location, and website per user. Ideal for PropTech SaaS vendors, mortgage product teams, and B2B service providers targeting real estate professionals.

ClawHub Agent Skills author: API Dojo v1.0.0 MIT-0 2 files body ≈ 728 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Finds real estate agents, brokers, property investors, and real estate professionals on Twitter/X using apidojo's Twitter User Scraper on Apify.

As a process B 72/100 · Nearly there — weak spots: result and completion, progress reporting

AnalyzerSales 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%
85
Run on models
none yet
Process rating
B
72/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
50
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 72/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 728 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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 746: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 8 items
    • +4Has examples (3 code blocks)
    • +1License stated

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

    External checks

    ClawHub: suspicious
    The skill is a coherent Twitter/X prospecting integration, but it exposes broad social-graph scraping and arbitrary mapping options without enough scoping or user warnings.
    LLM: suspicious (high) · VirusTotal: · 3 Sept 2026