SKILLEMALL.ai

AC sg-property-scraper

Search Singapore property rental and sale listings with flexible filters. Use when asked to search Singapore properties, find rental or sale listings, check property prices near MRT stations, or compare commute times. Supports filtering by listing type (rent/sale), property type (HDB/Condo/Landed), bedrooms, bathrooms, price range, size, TOP year, MRT station codes, distance to MRT, room type, availability, and commute time to a destination. Outputs JSON to stdout.

ClawHub Agent Skills author: 5kbpers v1.0.1 5 files body ≈ 1 432 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, progress reporting

ProcedureSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 5. 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 61/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1432 tokens
    • 100Running it twice. No mutating operations
    • low 11 top-level sections: this looks like several domains in one skill

    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 469: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 20 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: clean
    The skill is a disclosed Singapore property-listing scraper with optional Google commute calculations, and the flagged behaviors are purpose-aligned rather than hidden or deceptive.
    LLM: benign (high) · VirusTotal: suspicious · 27 May 2026