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AB apify-booking-host-leads

Find and enrich B2B leads from Booking.com - hotels, apartments, and vacation rentals - and pull each host's or property manager's real contact details (email, phone, company name, registration number). Use when the user says things like "get emails from Booking.com", "Booking.com lead generation", "find tour operator / property manager / host contacts", "scrape accommodation owner emails", or "build a list of Booking hosts". The host's email is usually already inside the Booking scraper's traderInfo field (EU/AU trader-transparency disclosure) - this skill leads with that and uses a Google Maps email scraper only as a fallback, instead of relying on Google Maps first (which mostly returns the wrong business).

apify/awesome-skills Agent Skills author: apify 2 files body ≈ 2 039 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Find and enrich B2B leads from Booking.com - hotels, apartments, and vacation rentals - and pull each host's or property manager's real contact details…

As a process B 68/100 · Nearly there — weak spots: running it twice, progress reporting

ProcedureSales and CRMData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
68/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
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

    • note frontmatter-key unknown frontmatter key "author_url"

    Process rating: all ten parameters 68/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 16 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2039 tokens
    • low The response is described with custom markup (7 tags): a typed call is more reliable

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 719: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 16 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.